11_Support Vector Machine - Detección de URLs maliciosas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/drive/1IxiJ0fjBvuVslq86bR-WogzvKGoVYlJz\">Abre este Jupyter en Google Colab</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Caso Práctico: _Support Vector Machine (SVM)_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conjunto de datos: Detección de URLs maliciosas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descripción\n",
    "The Web has long become a major platform for online criminal activities. URLs are used as the main vehicle in this domain. To counter this issues security community focused its efforts on developing techniques for mostly blacklisting of malicious URLs.\n",
    "\n",
    "While successful in protecting users from known malicious domains, this approach only solves part of the problem. The new malicious URLs that sprang up all over the web in masses commonly get a head start in this race. Besides that, Alexa ranked, trusted websites may convey compromised fraudulent URLs called defacement URL.\n",
    "\n",
    "We study mainly five different types of URLs:\n",
    "\n",
    "**Benign URLs**: Over 35,300 benign URLs were collected from Alexa top websites. The domains have been passed through a Heritrix web crawler to extract the URLs. Around half a million unique URLs are crawled initially and then passed to remove duplicate and domain only URLs. Later the extracted URLs have been checked through Virustotal to filter the benign URLs.\n",
    "\n",
    "**Spam URLs**: Around 12,000 spam URLs were collected from the publicly available WEBSPAM-UK2007 dataset.\n",
    "\n",
    "**Phishing URLs**: Around 10,000 phishing URLs were taken from OpenPhish which is a repository of active phishing sites.\n",
    "\n",
    "**Malware URLs**: More than 11,500 URLs related to malware websites were obtained from DNS-BH which is a project that maintain list of malware sites.\n",
    "\n",
    "**Defacement URLs**: More than 45,450 URLs belong to Defacement URL category. They are Alexa ranked trusted websites hosting fraudulent or hidden URL that contains both malicious web pages.\n",
    "\n",
    "### Descarga de los ficheros de datos\n",
    "https://www.unb.ca/cic/datasets/url-2016.html\n",
    "\n",
    "### Referencias adicionales sobre el conjunto de datos\n",
    "_Mohammad Saiful Islam Mamun, Mohammad Ahmad Rathore, Arash Habibi Lashkari, Natalia Stakhanova and Ali A. Ghorbani, \"Detecting Malicious URLs Using Lexical Analysis\", Network and System Security, Springer International Publishing, P467--482, 2016._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "# Ignoramos algunos warnings que se producen por invocar el pipeline sin el nombre de las características\n",
    "warnings.filterwarnings('ignore', category=UserWarning, message='.*X does not have valid feature names.*')\n",
    "warnings.filterwarnings('ignore', category=RuntimeWarning, message='.*invalid value encountered in subtract.*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones auxiliares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construcción de una función que realice el particionado completo\n",
    "def train_val_test_split(df, rstate=42, shuffle=True, stratify=None):\n",
    "    strat = df[stratify] if stratify else None\n",
    "    train_set, test_set = train_test_split(\n",
    "        df, test_size=0.4, random_state=rstate, shuffle=shuffle, stratify=strat)\n",
    "    strat = test_set[stratify] if stratify else None\n",
    "    val_set, test_set = train_test_split(\n",
    "        test_set, test_size=0.5, random_state=rstate, shuffle=shuffle, stratify=strat)\n",
    "    return (train_set, val_set, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Representación gráfica del límite de decisión\n",
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Lectura del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"datasets/FinalDataset/Phishing.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Visualización preliminar de la información"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Querylength  domain_token_count  path_token_count  avgdomaintokenlen  \\\n",
       "0            0                   2                12                5.5   \n",
       "1            0                   3                12                5.0   \n",
       "2            2                   2                11                4.0   \n",
       "3            0                   2                 7                4.5   \n",
       "4           19                   2                10                6.0   \n",
       "5            0                   2                10                5.5   \n",
       "6            0                   2                12                4.5   \n",
       "7            0                   2                11                3.5   \n",
       "8            0                   2                 9                2.5   \n",
       "9            0                   2                13                4.5   \n",
       "\n",
       "   longdomaintokenlen  avgpathtokenlen  tld  charcompvowels  charcompace  \\\n",
       "0                   8         4.083334    2              15            7   \n",
       "1                  10         3.583333    3              12            8   \n",
       "2                   5         4.750000    2              16           11   \n",
       "3                   7         5.714286    2              15           10   \n",
       "4                   9         2.250000    2               9            5   \n",
       "5                   9         4.100000    2              15           11   \n",
       "6                   6         5.333334    2              24            9   \n",
       "7                   4         3.909091    2              15            6   \n",
       "8                   3         4.555555    2               6            3   \n",
       "9                   6         5.307692    2              16            9   \n",
       "\n",
       "   ldl_url  ...  SymbolCount_FileName  SymbolCount_Extension  \\\n",
       "0        0  ...                    -1                     -1   \n",
       "1        2  ...                     1                      0   \n",
       "2        0  ...                     2                      0   \n",
       "3        0  ...                     0                      0   \n",
       "4        0  ...                     5                      4   \n",
       "5        0  ...                    -1                     -1   \n",
       "6        0  ...                     0                      0   \n",
       "7        0  ...                     0                      0   \n",
       "8        0  ...                     1                      0   \n",
       "9        1  ...                    -1                     -1   \n",
       "\n",
       "   SymbolCount_Afterpath  Entropy_URL  Entropy_Domain  Entropy_DirectoryName  \\\n",
       "0                     -1     0.676804        0.860529              -1.000000   \n",
       "1                     -1     0.715629        0.776796               0.693127   \n",
       "2                      1     0.677701        1.000000               0.677704   \n",
       "3                     -1     0.696067        0.879588               0.818007   \n",
       "4                      3     0.747202        0.833700               0.655459   \n",
       "5                     -1     0.732981        0.860529              -1.000000   \n",
       "6                     -1     0.692383        0.939794               0.910795   \n",
       "7                     -1     0.707365        0.916667               0.916667   \n",
       "8                     -1     0.742606        1.000000               0.785719   \n",
       "9                     -1     0.734633        0.939794              -1.000000   \n",
       "\n",
       "   Entropy_Filename  Entropy_Extension  Entropy_Afterpath  URL_Type_obf_Type  \n",
       "0         -1.000000           -1.00000          -1.000000             benign  \n",
       "1          0.738315            1.00000          -1.000000             benign  \n",
       "2          0.916667            0.00000           0.898227             benign  \n",
       "3          0.753585            0.00000          -1.000000             benign  \n",
       "4          0.829535            0.83615           0.823008             benign  \n",
       "5         -1.000000           -1.00000          -1.000000             benign  \n",
       "6          0.673973            0.00000          -1.000000             benign  \n",
       "7          0.690332            0.00000          -1.000000             benign  \n",
       "8          0.808833            1.00000          -1.000000             benign  \n",
       "9         -1.000000           -1.00000          -1.000000             benign  \n",
       "\n",
       "[10 rows x 80 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>domain_token_count</th>\n",
       "      <th>path_token_count</th>\n",
       "      <th>avgdomaintokenlen</th>\n",
       "      <th>longdomaintokenlen</th>\n",
       "      <th>avgpathtokenlen</th>\n",
       "      <th>tld</th>\n",
       "      <th>charcompvowels</th>\n",
       "      <th>charcompace</th>\n",
       "      <th>ldl_url</th>\n",
       "      <th>...</th>\n",
       "      <th>SymbolCount_Directoryname</th>\n",
       "      <th>SymbolCount_FileName</th>\n",
       "      <th>SymbolCount_Extension</th>\n",
       "      <th>SymbolCount_Afterpath</th>\n",
       "      <th>Entropy_URL</th>\n",
       "      <th>Entropy_Domain</th>\n",
       "      <th>Entropy_DirectoryName</th>\n",
       "      <th>Entropy_Filename</th>\n",
       "      <th>Entropy_Extension</th>\n",
       "      <th>Entropy_Afterpath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15096.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>15367.000000</td>\n",
       "      <td>13541.000000</td>\n",
       "      <td>15177.000000</td>\n",
       "      <td>15364.000000</td>\n",
       "      <td>15364.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.446021</td>\n",
       "      <td>2.543698</td>\n",
       "      <td>8.477061</td>\n",
       "      <td>5.851956</td>\n",
       "      <td>10.027461</td>\n",
       "      <td>5.289936</td>\n",
       "      <td>2.543698</td>\n",
       "      <td>12.659986</td>\n",
       "      <td>8.398516</td>\n",
       "      <td>1.910913</td>\n",
       "      <td>...</td>\n",
       "      <td>2.120843</td>\n",
       "      <td>1.124618</td>\n",
       "      <td>0.500813</td>\n",
       "      <td>-0.158782</td>\n",
       "      <td>0.721684</td>\n",
       "      <td>0.854232</td>\n",
       "      <td>0.634859</td>\n",
       "      <td>0.682896</td>\n",
       "      <td>0.313617</td>\n",
       "      <td>-0.723793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.151453</td>\n",
       "      <td>0.944938</td>\n",
       "      <td>4.660250</td>\n",
       "      <td>2.064581</td>\n",
       "      <td>5.281090</td>\n",
       "      <td>3.535097</td>\n",
       "      <td>0.944938</td>\n",
       "      <td>8.562206</td>\n",
       "      <td>6.329007</td>\n",
       "      <td>4.657731</td>\n",
       "      <td>...</td>\n",
       "      <td>2.777307</td>\n",
       "      <td>2.570246</td>\n",
       "      <td>2.261013</td>\n",
       "      <td>2.535939</td>\n",
       "      <td>0.049246</td>\n",
       "      <td>0.072641</td>\n",
       "      <td>0.510992</td>\n",
       "      <td>0.502288</td>\n",
       "      <td>0.576910</td>\n",
       "      <td>0.649785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.419560</td>\n",
       "      <td>0.561913</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.687215</td>\n",
       "      <td>0.798231</td>\n",
       "      <td>0.709532</td>\n",
       "      <td>0.707165</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.723217</td>\n",
       "      <td>0.859793</td>\n",
       "      <td>0.785949</td>\n",
       "      <td>0.814038</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>6.666666</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.571429</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.757949</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.859582</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>173.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.869701</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Querylength  domain_token_count  path_token_count  avgdomaintokenlen  \\\n",
       "count  15367.000000        15367.000000      15367.000000       15367.000000   \n",
       "mean       3.446021            2.543698          8.477061           5.851956   \n",
       "std       14.151453            0.944938          4.660250           2.064581   \n",
       "min        0.000000            2.000000          0.000000           1.500000   \n",
       "25%        0.000000            2.000000          5.000000           4.500000   \n",
       "50%        0.000000            2.000000          8.000000           5.500000   \n",
       "75%        0.000000            3.000000         11.000000           6.666666   \n",
       "max      173.000000           19.000000         68.000000          29.500000   \n",
       "\n",
       "       longdomaintokenlen  avgpathtokenlen           tld  charcompvowels  \\\n",
       "count        15367.000000     15096.000000  15367.000000    15367.000000   \n",
       "mean            10.027461         5.289936      2.543698       12.659986   \n",
       "std              5.281090         3.535097      0.944938        8.562206   \n",
       "min              2.000000         0.000000      2.000000        0.000000   \n",
       "25%              7.000000         3.800000      2.000000        6.000000   \n",
       "50%              9.000000         4.500000      2.000000       11.000000   \n",
       "75%             12.000000         5.571429      3.000000       17.000000   \n",
       "max             63.000000       105.000000     19.000000       94.000000   \n",
       "\n",
       "        charcompace       ldl_url  ...  SymbolCount_Directoryname  \\\n",
       "count  15367.000000  15367.000000  ...               15367.000000   \n",
       "mean       8.398516      1.910913  ...                   2.120843   \n",
       "std        6.329007      4.657731  ...                   2.777307   \n",
       "min        0.000000      0.000000  ...                  -1.000000   \n",
       "25%        4.000000      0.000000  ...                   1.000000   \n",
       "50%        7.000000      0.000000  ...                   2.000000   \n",
       "75%       11.000000      1.000000  ...                   3.000000   \n",
       "max       62.000000     58.000000  ...                  24.000000   \n",
       "\n",
       "       SymbolCount_FileName  SymbolCount_Extension  SymbolCount_Afterpath  \\\n",
       "count          15367.000000           15367.000000           15367.000000   \n",
       "mean               1.124618               0.500813              -0.158782   \n",
       "std                2.570246               2.261013               2.535939   \n",
       "min               -1.000000              -1.000000              -1.000000   \n",
       "25%                0.000000               0.000000              -1.000000   \n",
       "50%                0.000000               0.000000              -1.000000   \n",
       "75%                1.000000               0.000000              -1.000000   \n",
       "max               31.000000              30.000000              29.000000   \n",
       "\n",
       "        Entropy_URL  Entropy_Domain  Entropy_DirectoryName  Entropy_Filename  \\\n",
       "count  15367.000000    15367.000000           13541.000000      15177.000000   \n",
       "mean       0.721684        0.854232               0.634859          0.682896   \n",
       "std        0.049246        0.072641               0.510992          0.502288   \n",
       "min        0.419560        0.561913              -1.000000         -1.000000   \n",
       "25%        0.687215        0.798231               0.709532          0.707165   \n",
       "50%        0.723217        0.859793               0.785949          0.814038   \n",
       "75%        0.757949        0.916667               0.859582          0.916667   \n",
       "max        0.869701        1.000000               0.962479          1.000000   \n",
       "\n",
       "       Entropy_Extension  Entropy_Afterpath  \n",
       "count       15364.000000       15364.000000  \n",
       "mean            0.313617          -0.723793  \n",
       "std             0.576910           0.649785  \n",
       "min            -1.000000          -1.000000  \n",
       "25%             0.000000          -1.000000  \n",
       "50%             0.000000          -1.000000  \n",
       "75%             1.000000          -1.000000  \n",
       "max             1.000000           1.000000  \n",
       "\n",
       "[8 rows x 79 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15367 entries, 0 to 15366\n",
      "Data columns (total 80 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Querylength                      15367 non-null  int64  \n",
      " 1   domain_token_count               15367 non-null  int64  \n",
      " 2   path_token_count                 15367 non-null  int64  \n",
      " 3   avgdomaintokenlen                15367 non-null  float64\n",
      " 4   longdomaintokenlen               15367 non-null  int64  \n",
      " 5   avgpathtokenlen                  15096 non-null  float64\n",
      " 6   tld                              15367 non-null  int64  \n",
      " 7   charcompvowels                   15367 non-null  int64  \n",
      " 8   charcompace                      15367 non-null  int64  \n",
      " 9   ldl_url                          15367 non-null  int64  \n",
      " 10  ldl_domain                       15367 non-null  int64  \n",
      " 11  ldl_path                         15367 non-null  int64  \n",
      " 12  ldl_filename                     15367 non-null  int64  \n",
      " 13  ldl_getArg                       15367 non-null  int64  \n",
      " 14  dld_url                          15367 non-null  int64  \n",
      " 15  dld_domain                       15367 non-null  int64  \n",
      " 16  dld_path                         15367 non-null  int64  \n",
      " 17  dld_filename                     15367 non-null  int64  \n",
      " 18  dld_getArg                       15367 non-null  int64  \n",
      " 19  urlLen                           15367 non-null  int64  \n",
      " 20  domainlength                     15367 non-null  int64  \n",
      " 21  pathLength                       15367 non-null  int64  \n",
      " 22  subDirLen                        15367 non-null  int64  \n",
      " 23  fileNameLen                      15367 non-null  int64  \n",
      " 24  this.fileExtLen                  15367 non-null  int64  \n",
      " 25  ArgLen                           15367 non-null  int64  \n",
      " 26  pathurlRatio                     15367 non-null  float64\n",
      " 27  ArgUrlRatio                      15367 non-null  float64\n",
      " 28  argDomanRatio                    15367 non-null  float64\n",
      " 29  domainUrlRatio                   15367 non-null  float64\n",
      " 30  pathDomainRatio                  15367 non-null  float64\n",
      " 31  argPathRatio                     15367 non-null  float64\n",
      " 32  executable                       15367 non-null  int64  \n",
      " 33  isPortEighty                     15367 non-null  int64  \n",
      " 34  NumberofDotsinURL                15367 non-null  int64  \n",
      " 35  ISIpAddressInDomainName          15367 non-null  int64  \n",
      " 36  CharacterContinuityRate          15367 non-null  float64\n",
      " 37  LongestVariableValue             15367 non-null  int64  \n",
      " 38  URL_DigitCount                   15367 non-null  int64  \n",
      " 39  host_DigitCount                  15367 non-null  int64  \n",
      " 40  Directory_DigitCount             15367 non-null  int64  \n",
      " 41  File_name_DigitCount             15367 non-null  int64  \n",
      " 42  Extension_DigitCount             15367 non-null  int64  \n",
      " 43  Query_DigitCount                 15367 non-null  int64  \n",
      " 44  URL_Letter_Count                 15367 non-null  int64  \n",
      " 45  host_letter_count                15367 non-null  int64  \n",
      " 46  Directory_LetterCount            15367 non-null  int64  \n",
      " 47  Filename_LetterCount             15367 non-null  int64  \n",
      " 48  Extension_LetterCount            15367 non-null  int64  \n",
      " 49  Query_LetterCount                15367 non-null  int64  \n",
      " 50  LongestPathTokenLength           15367 non-null  int64  \n",
      " 51  Domain_LongestWordLength         15367 non-null  int64  \n",
      " 52  Path_LongestWordLength           15367 non-null  int64  \n",
      " 53  sub-Directory_LongestWordLength  15367 non-null  int64  \n",
      " 54  Arguments_LongestWordLength      15367 non-null  int64  \n",
      " 55  URL_sensitiveWord                15367 non-null  int64  \n",
      " 56  URLQueries_variable              15367 non-null  int64  \n",
      " 57  spcharUrl                        15367 non-null  int64  \n",
      " 58  delimeter_Domain                 15367 non-null  int64  \n",
      " 59  delimeter_path                   15367 non-null  int64  \n",
      " 60  delimeter_Count                  15367 non-null  int64  \n",
      " 61  NumberRate_URL                   15367 non-null  float64\n",
      " 62  NumberRate_Domain                15367 non-null  float64\n",
      " 63  NumberRate_DirectoryName         15358 non-null  float64\n",
      " 64  NumberRate_FileName              15358 non-null  float64\n",
      " 65  NumberRate_Extension             8012 non-null   float64\n",
      " 66  NumberRate_AfterPath             15364 non-null  float64\n",
      " 67  SymbolCount_URL                  15367 non-null  int64  \n",
      " 68  SymbolCount_Domain               15367 non-null  int64  \n",
      " 69  SymbolCount_Directoryname        15367 non-null  int64  \n",
      " 70  SymbolCount_FileName             15367 non-null  int64  \n",
      " 71  SymbolCount_Extension            15367 non-null  int64  \n",
      " 72  SymbolCount_Afterpath            15367 non-null  int64  \n",
      " 73  Entropy_URL                      15367 non-null  float64\n",
      " 74  Entropy_Domain                   15367 non-null  float64\n",
      " 75  Entropy_DirectoryName            13541 non-null  float64\n",
      " 76  Entropy_Filename                 15177 non-null  float64\n",
      " 77  Entropy_Extension                15364 non-null  float64\n",
      " 78  Entropy_Afterpath                15364 non-null  float64\n",
      " 79  URL_Type_obf_Type                15367 non-null  object \n",
      "dtypes: float64(21), int64(58), object(1)\n",
      "memory usage: 9.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "URL_Type_obf_Type\n",
       "benign      7781\n",
       "phishing    7586\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"URL_Type_obf_Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "avgpathtokenlen             True\n",
       "NumberRate_DirectoryName    True\n",
       "NumberRate_FileName         True\n",
       "NumberRate_Extension        True\n",
       "NumberRate_AfterPath        True\n",
       "Entropy_DirectoryName       True\n",
       "Entropy_Filename            True\n",
       "Entropy_Extension           True\n",
       "Entropy_Afterpath           True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comprobación de si existen valores nulos\n",
    "is_null = df.isna().any()\n",
    "is_null[is_null]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "argPathRatio    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comprobación de la existencia de valores infinitos\n",
    "is_inf = df.isin([np.inf, -np.inf]).any()\n",
    "is_inf[is_inf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representación gráfica de dos características\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(df[\"domainUrlRatio\"][df['URL_Type_obf_Type'] == \"phishing\"], df[\"domainlength\"][df['URL_Type_obf_Type'] == \"phishing\"], c=\"r\", marker=\".\")\n",
    "plt.scatter(df[\"domainUrlRatio\"][df['URL_Type_obf_Type'] == \"benign\"], df[\"domainlength\"][df['URL_Type_obf_Type'] == \"benign\"], c=\"g\", marker=\"x\")\n",
    "plt.xlabel(\"domainUrlRatio\", fontsize=13)\n",
    "plt.ylabel(\"domainlength\", fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. División del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# División del conjunto de datos\n",
    "train_set, val_set, test_set = train_val_test_split(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_set.drop(\"URL_Type_obf_Type\", axis=1)\n",
    "y_train = train_set[\"URL_Type_obf_Type\"].copy()\n",
    "\n",
    "X_val = val_set.drop(\"URL_Type_obf_Type\", axis=1)\n",
    "y_val = val_set[\"URL_Type_obf_Type\"].copy()\n",
    "\n",
    "X_test = test_set.drop(\"URL_Type_obf_Type\", axis=1)\n",
    "y_test = test_set[\"URL_Type_obf_Type\"].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Preparación del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminamos el atributo que tiene valores infinitos\n",
    "X_train = X_train.drop(\"argPathRatio\", axis=1)\n",
    "X_val = X_val.drop(\"argPathRatio\", axis=1)\n",
    "X_test = X_test.drop(\"argPathRatio\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rellenamos los valores nulos con la mediana\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy=\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rellenamos los valores nulos\n",
    "X_train_prep = imputer.fit_transform(X_train)\n",
    "X_val_prep = imputer.fit_transform(X_val)\n",
    "X_test_prep = imputer.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transformamos el resultado a un DataFrame de Pandas\n",
    "X_train_prep = pd.DataFrame(X_train_prep, columns=X_train.columns, index=y_train.index)\n",
    "X_val_prep = pd.DataFrame(X_val_prep, columns=X_val.columns, index=y_val.index)\n",
    "X_test_prep = pd.DataFrame(X_test_prep, columns=X_test.columns, index=y_test.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Querylength</th>\n",
       "      <th>domain_token_count</th>\n",
       "      <th>path_token_count</th>\n",
       "      <th>avgdomaintokenlen</th>\n",
       "      <th>longdomaintokenlen</th>\n",
       "      <th>avgpathtokenlen</th>\n",
       "      <th>tld</th>\n",
       "      <th>charcompvowels</th>\n",
       "      <th>charcompace</th>\n",
       "      <th>ldl_url</th>\n",
       "      <th>...</th>\n",
       "      <th>SymbolCount_Directoryname</th>\n",
       "      <th>SymbolCount_FileName</th>\n",
       "      <th>SymbolCount_Extension</th>\n",
       "      <th>SymbolCount_Afterpath</th>\n",
       "      <th>Entropy_URL</th>\n",
       "      <th>Entropy_Domain</th>\n",
       "      <th>Entropy_DirectoryName</th>\n",
       "      <th>Entropy_Filename</th>\n",
       "      <th>Entropy_Extension</th>\n",
       "      <th>Entropy_Afterpath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2134</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.681183</td>\n",
       "      <td>0.827729</td>\n",
       "      <td>0.702637</td>\n",
       "      <td>0.849605</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9178</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.695232</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>0.682849</td>\n",
       "      <td>0.875578</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.778747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13622</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.666666</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.836006</td>\n",
       "      <td>0.869991</td>\n",
       "      <td>0.879588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15182</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.731804</td>\n",
       "      <td>0.796490</td>\n",
       "      <td>0.796658</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8013</th>\n",
       "      <td>74.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.875000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.653371</td>\n",
       "      <td>0.820569</td>\n",
       "      <td>0.758055</td>\n",
       "      <td>0.714969</td>\n",
       "      <td>0.712215</td>\n",
       "      <td>0.708031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12408</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.726479</td>\n",
       "      <td>0.789538</td>\n",
       "      <td>0.800705</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.678515</td>\n",
       "      <td>0.796658</td>\n",
       "      <td>0.871049</td>\n",
       "      <td>0.695112</td>\n",
       "      <td>0.701662</td>\n",
       "      <td>0.698106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10714</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.666666</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.745348</td>\n",
       "      <td>0.869991</td>\n",
       "      <td>0.788921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3986</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.760843</td>\n",
       "      <td>0.798231</td>\n",
       "      <td>0.822491</td>\n",
       "      <td>0.796670</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.709062</td>\n",
       "      <td>0.929897</td>\n",
       "      <td>0.884735</td>\n",
       "      <td>0.674994</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Querylength  domain_token_count  path_token_count  avgdomaintokenlen  \\\n",
       "2134           0.0                 2.0               6.0           2.000000   \n",
       "9178           0.0                 4.0              18.0           3.250000   \n",
       "13622          0.0                 3.0               3.0           6.666666   \n",
       "15182          0.0                 3.0               5.0           3.333333   \n",
       "8013          74.0                 2.0              13.0           9.500000   \n",
       "12408          0.0                 3.0               4.0           8.333333   \n",
       "509           20.0                 2.0              13.0           4.500000   \n",
       "10714          0.0                 3.0               8.0           6.666666   \n",
       "3986           0.0                 2.0               6.0           6.500000   \n",
       "748            0.0                 2.0               8.0           4.000000   \n",
       "\n",
       "       longdomaintokenlen  avgpathtokenlen  tld  charcompvowels  charcompace  \\\n",
       "2134                  2.0         8.666667  2.0            17.0         10.0   \n",
       "9178                  5.0         1.000000  4.0            18.0         13.0   \n",
       "13622                14.0         4.000000  3.0             1.0          1.0   \n",
       "15182                 4.0         3.000000  3.0             5.0          2.0   \n",
       "8013                 17.0         7.875000  2.0            21.0         29.0   \n",
       "12408                19.0         3.750000  3.0             5.0          1.0   \n",
       "509                   6.0         3.000000  2.0            24.0         17.0   \n",
       "10714                14.0         4.250000  3.0            11.0          5.0   \n",
       "3986                 10.0         4.500000  2.0             7.0          7.0   \n",
       "748                   5.0         5.750000  2.0            14.0         14.0   \n",
       "\n",
       "       ldl_url  ...  SymbolCount_Directoryname  SymbolCount_FileName  \\\n",
       "2134       0.0  ...                        2.0                   0.0   \n",
       "9178       2.0  ...                       12.0                   3.0   \n",
       "13622      1.0  ...                        1.0                   0.0   \n",
       "15182      0.0  ...                        2.0                   1.0   \n",
       "8013      26.0  ...                        4.0                   5.0   \n",
       "12408      0.0  ...                        2.0                   0.0   \n",
       "509        0.0  ...                        1.0                  14.0   \n",
       "10714      0.0  ...                        4.0                   0.0   \n",
       "3986       0.0  ...                        2.0                   0.0   \n",
       "748        1.0  ...                        2.0                   0.0   \n",
       "\n",
       "       SymbolCount_Extension  SymbolCount_Afterpath  Entropy_URL  \\\n",
       "2134                     0.0                   -1.0     0.681183   \n",
       "9178                     0.0                    4.0     0.695232   \n",
       "13622                    0.0                   -1.0     0.836006   \n",
       "15182                    0.0                   -1.0     0.731804   \n",
       "8013                     4.0                    3.0     0.653371   \n",
       "12408                    0.0                   -1.0     0.726479   \n",
       "509                     13.0                   12.0     0.678515   \n",
       "10714                    0.0                   -1.0     0.745348   \n",
       "3986                     0.0                   -1.0     0.760843   \n",
       "748                      0.0                   -1.0     0.709062   \n",
       "\n",
       "       Entropy_Domain  Entropy_DirectoryName  Entropy_Filename  \\\n",
       "2134         0.827729               0.702637          0.849605   \n",
       "9178         0.820160               0.682849          0.875578   \n",
       "13622        0.869991               0.879588          1.000000   \n",
       "15182        0.796490               0.796658          1.000000   \n",
       "8013         0.820569               0.758055          0.714969   \n",
       "12408        0.789538               0.800705          1.000000   \n",
       "509          0.796658               0.871049          0.695112   \n",
       "10714        0.869991               0.788921          1.000000   \n",
       "3986         0.798231               0.822491          0.796670   \n",
       "748          0.929897               0.884735          0.674994   \n",
       "\n",
       "       Entropy_Extension  Entropy_Afterpath  \n",
       "2134            0.000000          -1.000000  \n",
       "9178            0.000000           0.778747  \n",
       "13622           0.000000          -1.000000  \n",
       "15182           1.000000          -1.000000  \n",
       "8013            0.712215           0.708031  \n",
       "12408           0.000000          -1.000000  \n",
       "509             0.701662           0.698106  \n",
       "10714           0.000000          -1.000000  \n",
       "3986            0.000000          -1.000000  \n",
       "748             0.000000          -1.000000  \n",
       "\n",
       "[10 rows x 78 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_prep.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: bool)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comprobamos si hay valores nulos en el conjunto de datos de entrenamiento\n",
    "is_null = X_train_prep.isna().any()\n",
    "is_null[is_null]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. SMV: Kernel lineal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Conjunto de datos reducido"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Entrenamiento del algoritmo con un conjunto de datos reducido**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reducimos el conjunto de datos para representarlo gráficamente\n",
    "X_train_reduced = X_train_prep[[\"domainUrlRatio\", \"domainlength\"]].copy()\n",
    "X_val_reduced = X_val_prep[[\"domainUrlRatio\", \"domainlength\"]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domainUrlRatio</th>\n",
       "      <th>domainlength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2134</th>\n",
       "      <td>0.072464</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9178</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13622</th>\n",
       "      <td>0.511628</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15182</th>\n",
       "      <td>0.315789</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8013</th>\n",
       "      <td>0.107527</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>0.116667</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13418</th>\n",
       "      <td>0.477273</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>0.157895</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>0.072917</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>0.207547</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9220 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       domainUrlRatio  domainlength\n",
       "2134         0.072464           5.0\n",
       "9178         0.166667          16.0\n",
       "13622        0.511628          22.0\n",
       "15182        0.315789          12.0\n",
       "8013         0.107527          20.0\n",
       "...               ...           ...\n",
       "5191         0.116667          14.0\n",
       "13418        0.477273          21.0\n",
       "5390         0.157895           9.0\n",
       "860          0.072917           7.0\n",
       "7270         0.207547          11.0\n",
       "\n",
       "[9220 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=50, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=50, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=50, kernel='linear')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# SVM Large Margin Classification\n",
    "svm_clf = SVC(kernel=\"linear\", C=50)\n",
    "svm_clf.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Representación del límite de decisión**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(X_train_reduced.values[:, 0][y_train==\"phishing\"], X_train_reduced.values[:, 1][y_train==\"phishing\"], \"g^\")\n",
    "plt.plot(X_train_reduced.values[:, 0][y_train==\"benign\"], X_train_reduced.values[:, 1][y_train==\"benign\"], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf, 0, 1)\n",
    "plt.title(\"$C = {}$\".format(svm_clf.C), fontsize=16)\n",
    "plt.axis([0, 1, -100, 250])\n",
    "plt.xlabel(\"domainUrlRatio\", fontsize=13)\n",
    "plt.ylabel(\"domainlength\", fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicción con un conjunto de datos reducido**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_clf.predict(X_val_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.8142614601018676\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score:\", f1_score(y_pred, y_val, pos_label='phishing'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como se verá más adelante, para determinados kernels es muy importante escalar el conjunto de datos. En ese caso, para el kernel lineal, no es tan relevante, aunque es posible que proporciones mejores resultados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, RobustScaler()),\n",
       "                (&#x27;linear_svc&#x27;, SVC(C=50, kernel=&#x27;linear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, RobustScaler()),\n",
       "                (&#x27;linear_svc&#x27;, SVC(C=50, kernel=&#x27;linear&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RobustScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.RobustScaler.html\">?<span>Documentation for RobustScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RobustScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=50, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', RobustScaler()),\n",
       "                ('linear_svc', SVC(C=50, kernel='linear'))])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf_sc = Pipeline([\n",
    "        (\"scaler\", RobustScaler()),\n",
    "        (\"linear_svc\", SVC(kernel=\"linear\", C=50)),\n",
    "    ])\n",
    "\n",
    "svm_clf_sc.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_clf_sc.predict(X_val_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.8141592920353983\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score:\", f1_score(y_pred, y_val, pos_label='phishing'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Conjunto de datos completo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=1, kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=1, kernel='linear')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Entrenamiento con todo el conjunto de datos\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(kernel=\"linear\", C=1)\n",
    "svm_clf.fit(X_train_prep, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_clf.predict(X_val_prep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.961133069828722\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score:\", f1_score(y_pred, y_val, pos_label='phishing'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. SMV: Kernel no lineal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1. Polynomial Kernel (I)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Entrenamiento del algoritmo con un conjunto de datos reducido**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para representar el límite de decisión tenemos que pasar la variable objetivo a numérica\n",
    "y_train_num = y_train.factorize()[0]\n",
    "y_val_num = y_val.factorize()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;poly_features&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;,\n",
       "                 LinearSVC(C=20, dual=True, loss=&#x27;hinge&#x27;, max_iter=100000,\n",
       "                           random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;poly_features&#x27;, PolynomialFeatures(degree=3)),\n",
       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;svm_clf&#x27;,\n",
       "                 LinearSVC(C=20, dual=True, loss=&#x27;hinge&#x27;, max_iter=100000,\n",
       "                           random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;PolynomialFeatures<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.PolynomialFeatures.html\">?<span>Documentation for PolynomialFeatures</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>PolynomialFeatures(degree=3)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearSVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC(C=20, dual=True, loss=&#x27;hinge&#x27;, max_iter=100000, random_state=42)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('poly_features', PolynomialFeatures(degree=3)),\n",
       "                ('scaler', StandardScaler()),\n",
       "                ('svm_clf',\n",
       "                 LinearSVC(C=20, dual=True, loss='hinge', max_iter=100000,\n",
       "                           random_state=42))])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polynomial_svm_clf = Pipeline([\n",
    "        (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", LinearSVC(C=20, loss=\"hinge\", random_state=42, max_iter=100000, dual=True))\n",
    "    ])\n",
    "\n",
    "polynomial_svm_clf.fit(X_train_reduced, y_train_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Representación del límite de decisión**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dataset(X, y):\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g.\")\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"b.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(15,5), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_dataset(X_train_reduced.values, y_train_num)\n",
    "plot_predictions(polynomial_svm_clf, [0, 1, -100, 250])\n",
    "plt.xlabel(\"domainUrlRatio\", fontsize=11)\n",
    "plt.ylabel(\"domainlength\", fontsize=11)\n",
    "plt.sca(axes[1])\n",
    "plot_predictions(polynomial_svm_clf, [0, 1, -100, 250])\n",
    "plt.xlabel(\"domainUrlRatio\", fontsize=11)\n",
    "plt.ylabel(\"domainlength\", fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicción con el conjunto de datos reducido**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = polynomial_svm_clf.predict(X_val_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.857451403887689\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score:\", f1_score(y_pred, y_val_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2. Polynomial Kernel (II)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existe una forma más sencilla de entrenar un algoritmo SVM que utilize polynomial kernel utilizando el parámetro **kernel** de la propia función implementada en sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Entrenamiento del algoritmo con un conjunto de datos reducido**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=20, coef0=10, kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=20, coef0=10, kernel=&#x27;poly&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=20, coef0=10, kernel='poly')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(kernel=\"poly\", degree=3, coef0=10, C=20)\n",
    "svm_clf.fit(X_train_reduced, y_train_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Representación del límite de decisión**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(15,5), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_dataset(X_train_reduced.values, y_train_num)\n",
    "plot_predictions(svm_clf, [0, 1, -100, 250])\n",
    "plt.xlabel(\"domainUrlRatio\", fontsize=11)\n",
    "plt.ylabel(\"domainlength\", fontsize=11)\n",
    "plt.sca(axes[1])\n",
    "plot_predictions(svm_clf, [0, 1, -100, 250])\n",
    "plt.xlabel(\"domainUrlRatio\", fontsize=11)\n",
    "plt.ylabel(\"domainlength\", fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicción con un conjunto de datos reducido**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_clf.predict(X_val_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.8249238062986793\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score:\", f1_score(y_pred, y_val_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicción con el conjunto de datos completo**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=40, coef0=10, kernel=&#x27;poly&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=40, coef0=10, kernel=&#x27;poly&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=40, coef0=10, kernel='poly')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(kernel=\"poly\", degree=3, coef0=10, C=40)\n",
    "svm_clf.fit(X_train_prep, y_train_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svm_clf.predict(X_val_prep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.9715984147952443\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score:\", f1_score(y_pred, y_val_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2. Gaussian Kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Entrenamiento del algoritmo con un conjunto de datos reducido**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, RobustScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=1000, gamma=0.5))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, RobustScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=1000, gamma=0.5))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RobustScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.RobustScaler.html\">?<span>Documentation for RobustScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RobustScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=1000, gamma=0.5)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', RobustScaler()),\n",
       "                ('svm_clf', SVC(C=1000, gamma=0.5))])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([\n",
    "            (\"scaler\", RobustScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=0.5, C=1000))\n",
    "        ])\n",
    "\n",
    "rbf_kernel_svm_clf.fit(X_train_reduced, y_train_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Representación del límite de decisión**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(15,5), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_dataset(X_train_reduced.values, y_train_num)\n",
    "plot_predictions(rbf_kernel_svm_clf, [0, 1, -100, 250])\n",
    "plt.xlabel(\"domainUrlRatio\", fontsize=11)\n",
    "plt.ylabel(\"domainlength\", fontsize=11)\n",
    "plt.sca(axes[1])\n",
    "plot_predictions(rbf_kernel_svm_clf, [0, 1, -100, 250])\n",
    "plt.xlabel(\"domainUrlRatio\", fontsize=11)\n",
    "plt.ylabel(\"domainlength\", fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicción con un conjunto de datos reducido**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rbf_kernel_svm_clf.predict(X_val_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.8617363344051447\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score:\", f1_score(y_pred, y_val_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicción con un conjunto de datos completo**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-8 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, RobustScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=1000, gamma=0.05))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, RobustScaler()),\n",
       "                (&#x27;svm_clf&#x27;, SVC(C=1000, gamma=0.05))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RobustScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.RobustScaler.html\">?<span>Documentation for RobustScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RobustScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=1000, gamma=0.05)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', RobustScaler()),\n",
       "                ('svm_clf', SVC(C=1000, gamma=0.05))])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([\n",
    "            (\"scaler\", RobustScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=0.05, C=1000))\n",
    "        ])\n",
    "\n",
    "rbf_kernel_svm_clf.fit(X_train_prep, y_train_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rbf_kernel_svm_clf.predict(X_val_prep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.9640522875816994\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score:\", f1_score(y_pred, y_val_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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