12_Árboles de decisión - Detección de malware en Android.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/drive/1IdshG6TZvpklMnoGujyTwX3YoC7YMXpN\">Abre este Jupyter en Google Colab</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Caso práctico: Árboles de decisión"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso de uso práctico se pretende resolver un problema de detección de malware en dispositivos Android mediante el análisis del tráfico de red que genera el dispositivo mediante el uso de árboles de decisión."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conjunto de datos: Detección de malware en Android"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descripción\n",
    "The sophisticated and advanced Android malware is able to identify the presence of the emulator used by the malware analyst and in response, alter its behavior to evade detection. To overcome this issue, we installed the Android applications on the real device and captured its network traffic. See our publicly available Android Sandbox.\n",
    "\n",
    "CICAAGM dataset is captured by installing the Android apps on the real smartphones semi-automated. The dataset is generated from 1900 applications with the following three categories:\n",
    "\n",
    "**1. Adware (250 apps)**\n",
    "* Airpush: Designed to deliver unsolicited advertisements to the user’s systems for information stealing.\n",
    "* Dowgin: Designed as an advertisement library that can also steal the user’s information.\n",
    "* Kemoge: Designed to take over a user’s Android device. This adware is a hybrid of botnet and disguises itself as popular apps via repackaging.\n",
    "* Mobidash: Designed to display ads and to compromise user’s personal information.\n",
    "* Shuanet: Similar to Kemoge, Shuanet also is designed to take over a user’s device.\n",
    "\n",
    "**2. General Malware (150 apps)**\n",
    "* AVpass: Designed to be distributed in the guise of a Clock app.\n",
    "* FakeAV: Designed as a scam that tricks user to purchase a full version of the software in order to re-mediate non-existing infections.\n",
    "* FakeFlash/FakePlayer: Designed as a fake Flash app in order to direct users to a website (after successfully installed).\n",
    "* GGtracker: Designed for SMS fraud (sends SMS messages to a premium-rate number) and information stealing.\n",
    "* Penetho: Designed as a fake service (hacktool for Android devices that can be used to crack the WiFi password). The malware is also able to infect the user’s computer via infected email attachment, fake updates, external media and infected documents.\n",
    "\n",
    "**3. Benign (1500 apps)**\n",
    "* 2015 GooglePlay market (top free popular and top free new)\n",
    "* 2016 GooglePlay market (top free popular and top free new)\n",
    "\n",
    "### Ficheros de datos\n",
    "* pcap files – the network traffic of both the malware and benign (20% malware and 80% benign)\n",
    "* <span style=\"color:green\">.csv files - the list of extracted network traffic features generated by the CIC-flowmeter</span>\n",
    "\n",
    "### Descarga de los ficheros de datos\n",
    "https://www.unb.ca/cic/datasets/android-adware.html\n",
    "\n",
    "### Referencias adicionales sobre el conjunto de datos\n",
    "_Arash Habibi Lashkari, Andi Fitriah A. Kadir, Hugo Gonzalez, Kenneth Fon Mbah and Ali A. Ghorbani, “Towards a Network-Based Framework for Android Malware Detection and Characterization”, In the proceeding of the 15th International Conference on Privacy, Security and Trust, PST, Calgary, Canada, 2017._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: graphviz in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (0.20.1)\n"
     ]
    }
   ],
   "source": [
    "# Instalamos graphviz\n",
    "!pip install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.metrics import confusion_matrix, recall_score, f1_score, precision_score\n",
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "# Ignoramos algunos warnings que se producen por invocar el modelo sin el nombre de las características\n",
    "warnings.filterwarnings('ignore', category=UserWarning, message='.*X does not have valid feature names.*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones auxiliares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_labels(df, label_name):\n",
    "    X = df.drop(label_name, axis=1)\n",
    "    y = df[label_name].copy()\n",
    "    return (X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_result(y_pred, y, y_prep_pred, y_prep, metric):\n",
    "    print(metric.__name__, \"WITHOUT preparation:\", metric(y_pred, y, average='weighted'))\n",
    "    print(metric.__name__, \"WITH preparation:\", metric(y_prep_pred, y_prep, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Lectura del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('datasets/TotalFeatures-ISCXFlowMeter.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Visualización del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "   duration  total_fpackets  total_bpackets  total_fpktl  total_bpktl  \\\n",
       "0   1020586             668            1641        35692      2276876   \n",
       "1     80794               1               1           75          124   \n",
       "2       998               3               0          187            0   \n",
       "3    189868               9               9         1448         6200   \n",
       "4    110577               4               6          528         1422   \n",
       "5    261876               7               6         1618          882   \n",
       "6        14               2               0          104            0   \n",
       "7     29675               1               1           71          213   \n",
       "8    806635               4               0          239            0   \n",
       "9     56620               3               2         1074          719   \n",
       "\n",
       "   min_fpktl  min_bpktl  max_fpktl  max_bpktl  mean_fpktl  ...  mean_idle  \\\n",
       "0         52         52        679       1390   53.431138  ...        0.0   \n",
       "1         75        124         75        124   75.000000  ...        0.0   \n",
       "2         52         -1         83         -1   62.333333  ...        0.0   \n",
       "3         52         52        706       1390  160.888889  ...        0.0   \n",
       "4         52         52        331       1005  132.000000  ...        0.0   \n",
       "5         52         52        730        477  231.142857  ...        0.0   \n",
       "6         52         -1         52         -1   52.000000  ...        0.0   \n",
       "7         71        213         71        213   71.000000  ...        0.0   \n",
       "8         52         -1         83         -1   59.750000  ...        0.0   \n",
       "9         52         52        592        667  358.000000  ...        0.0   \n",
       "\n",
       "   max_idle  std_idle  FFNEPD  Init_Win_bytes_forward  \\\n",
       "0        -1       0.0       2                 4194240   \n",
       "1        -1       0.0       2                       0   \n",
       "2        -1       0.0       4                  101888   \n",
       "3        -1       0.0       2                 4194240   \n",
       "4        -1       0.0       2                  155136   \n",
       "5        -1       0.0       2                 4194240   \n",
       "6        -1       0.0       3                    5824   \n",
       "7        -1       0.0       2                       0   \n",
       "8        -1       0.0       5                  107008   \n",
       "9        -1       0.0       3                  128512   \n",
       "\n",
       "   Init_Win_bytes_backward  RRT_samples_clnt  Act_data_pkt_forward  \\\n",
       "0                  1853440              1640                   668   \n",
       "1                        0                 0                     1   \n",
       "2                       -1                 0                     3   \n",
       "3                  2722560                 8                     9   \n",
       "4                    31232                 5                     4   \n",
       "5                   926720                 3                     7   \n",
       "6                       -1                 0                     2   \n",
       "7                        0                 0                     1   \n",
       "8                       -1                 0                     4   \n",
       "9                    10816                 1                     3   \n",
       "\n",
       "   min_seg_size_forward   calss  \n",
       "0                    32  benign  \n",
       "1                     0  benign  \n",
       "2                    32  benign  \n",
       "3                    32  benign  \n",
       "4                    32  benign  \n",
       "5                    32  benign  \n",
       "6                    32  benign  \n",
       "7                     0  benign  \n",
       "8                    32  benign  \n",
       "9                    32  benign  \n",
       "\n",
       "[10 rows x 80 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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      " 3   total_fpktl              631955 non-null  int64  \n",
      " 4   total_bpktl              631955 non-null  int64  \n",
      " 5   min_fpktl                631955 non-null  int64  \n",
      " 6   min_bpktl                631955 non-null  int64  \n",
      " 7   max_fpktl                631955 non-null  int64  \n",
      " 8   max_bpktl                631955 non-null  int64  \n",
      " 9   mean_fpktl               631955 non-null  float64\n",
      " 10  mean_bpktl               631955 non-null  float64\n",
      " 11  std_fpktl                631955 non-null  float64\n",
      " 12  std_bpktl                631955 non-null  float64\n",
      " 13  total_fiat               631955 non-null  int64  \n",
      " 14  total_biat               631955 non-null  int64  \n",
      " 15  min_fiat                 631955 non-null  int64  \n",
      " 16  min_biat                 631955 non-null  int64  \n",
      " 17  max_fiat                 631955 non-null  int64  \n",
      " 18  max_biat                 631955 non-null  int64  \n",
      " 19  mean_fiat                631955 non-null  float64\n",
      " 20  mean_biat                631955 non-null  float64\n",
      " 21  std_fiat                 631955 non-null  float64\n",
      " 22  std_biat                 631955 non-null  float64\n",
      " 23  fpsh_cnt                 631955 non-null  int64  \n",
      " 24  bpsh_cnt                 631955 non-null  int64  \n",
      " 25  furg_cnt                 631955 non-null  int64  \n",
      " 26  burg_cnt                 631955 non-null  int64  \n",
      " 27  total_fhlen              631955 non-null  int64  \n",
      " 28  total_bhlen              631955 non-null  int64  \n",
      " 29  fPktsPerSecond           631955 non-null  float64\n",
      " 30  bPktsPerSecond           631955 non-null  float64\n",
      " 31  flowPktsPerSecond        631955 non-null  float64\n",
      " 32  flowBytesPerSecond       631955 non-null  float64\n",
      " 33  min_flowpktl             631955 non-null  int64  \n",
      " 34  max_flowpktl             631955 non-null  int64  \n",
      " 35  mean_flowpktl            631955 non-null  float64\n",
      " 36  std_flowpktl             631955 non-null  float64\n",
      " 37  min_flowiat              631955 non-null  int64  \n",
      " 38  max_flowiat              631955 non-null  int64  \n",
      " 39  mean_flowiat             631955 non-null  float64\n",
      " 40  std_flowiat              631955 non-null  float64\n",
      " 41  flow_fin                 631955 non-null  int64  \n",
      " 42  flow_syn                 631955 non-null  int64  \n",
      " 43  flow_rst                 631955 non-null  int64  \n",
      " 44  flow_psh                 631955 non-null  int64  \n",
      " 45  flow_ack                 631955 non-null  int64  \n",
      " 46  flow_urg                 631955 non-null  int64  \n",
      " 47  flow_cwr                 631955 non-null  int64  \n",
      " 48  flow_ece                 631955 non-null  int64  \n",
      " 49  downUpRatio              631955 non-null  float64\n",
      " 50  avgPacketSize            631955 non-null  float64\n",
      " 51  fAvgSegmentSize          631955 non-null  float64\n",
      " 52  fHeaderBytes             631955 non-null  int64  \n",
      " 53  fAvgBytesPerBulk         631955 non-null  int64  \n",
      " 54  fAvgPacketsPerBulk       631955 non-null  int64  \n",
      " 55  fAvgBulkRate             631955 non-null  int64  \n",
      " 56  bVarianceDataBytes       631955 non-null  float64\n",
      " 57  bAvgSegmentSize          631955 non-null  int64  \n",
      " 58  bAvgBytesPerBulk         631955 non-null  int64  \n",
      " 59  bAvgPacketsPerBulk       631955 non-null  int64  \n",
      " 60  bAvgBulkRate             631955 non-null  int64  \n",
      " 61  sflow_fpacket            631955 non-null  int64  \n",
      " 62  sflow_fbytes             631955 non-null  int64  \n",
      " 63  sflow_bpacket            631955 non-null  int64  \n",
      " 64  sflow_bbytes             631955 non-null  int64  \n",
      " 65  min_active               631955 non-null  int64  \n",
      " 66  mean_active              631955 non-null  float64\n",
      " 67  max_active               631955 non-null  int64  \n",
      " 68  std_active               631955 non-null  float64\n",
      " 69  min_idle                 631955 non-null  int64  \n",
      " 70  mean_idle                631955 non-null  float64\n",
      " 71  max_idle                 631955 non-null  int64  \n",
      " 72  std_idle                 631955 non-null  float64\n",
      " 73  FFNEPD                   631955 non-null  int64  \n",
      " 74  Init_Win_bytes_forward   631955 non-null  int64  \n",
      " 75  Init_Win_bytes_backward  631955 non-null  int64  \n",
      " 76  RRT_samples_clnt         631955 non-null  int64  \n",
      " 77  Act_data_pkt_forward     631955 non-null  int64  \n",
      " 78  min_seg_size_forward     631955 non-null  int64  \n",
      " 79  calss                    631955 non-null  object \n",
      "dtypes: float64(24), int64(55), object(1)\n",
      "memory usage: 385.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "calss\n",
       "benign            471597\n",
       "asware            155613\n",
       "GeneralMalware      4745\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['calss'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Buscando correlaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copiamos el conjunto de datos y transformamos la variable de salida a numérica para calcular correlaciones\n",
    "X = df.copy()\n",
    "X['calss'] = X['calss'].factorize()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "calss                     1.000000\n",
       "flow_fin                  0.286175\n",
       "min_seg_size_forward      0.258352\n",
       "Init_Win_bytes_forward    0.129425\n",
       "std_fpktl                 0.123758\n",
       "                            ...   \n",
       "furg_cnt                       NaN\n",
       "burg_cnt                       NaN\n",
       "flow_urg                       NaN\n",
       "flow_cwr                       NaN\n",
       "flow_ece                       NaN\n",
       "Name: calss, Length: 80, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculamos correlaciones\n",
    "corr_matrix = X.corr()\n",
    "corr_matrix[\"calss\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>duration</th>\n",
       "      <th>total_fpackets</th>\n",
       "      <th>total_bpackets</th>\n",
       "      <th>total_fpktl</th>\n",
       "      <th>total_bpktl</th>\n",
       "      <th>min_fpktl</th>\n",
       "      <th>min_bpktl</th>\n",
       "      <th>max_fpktl</th>\n",
       "      <th>max_bpktl</th>\n",
       "      <th>mean_fpktl</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_idle</th>\n",
       "      <th>max_idle</th>\n",
       "      <th>std_idle</th>\n",
       "      <th>FFNEPD</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <th>RRT_samples_clnt</th>\n",
       "      <th>Act_data_pkt_forward</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <th>calss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004837</td>\n",
       "      <td>0.004011</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>0.003518</td>\n",
       "      <td>-0.064100</td>\n",
       "      <td>-0.027231</td>\n",
       "      <td>0.008761</td>\n",
       "      <td>0.042925</td>\n",
       "      <td>-0.043746</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998901</td>\n",
       "      <td>0.999458</td>\n",
       "      <td>0.047582</td>\n",
       "      <td>0.016532</td>\n",
       "      <td>0.027610</td>\n",
       "      <td>0.029712</td>\n",
       "      <td>0.003785</td>\n",
       "      <td>0.004838</td>\n",
       "      <td>0.082955</td>\n",
       "      <td>0.067066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_fpackets</th>\n",
       "      <td>0.004837</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924622</td>\n",
       "      <td>0.425756</td>\n",
       "      <td>0.904007</td>\n",
       "      <td>-0.018958</td>\n",
       "      <td>0.005252</td>\n",
       "      <td>0.024685</td>\n",
       "      <td>0.086255</td>\n",
       "      <td>-0.007910</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001614</td>\n",
       "      <td>0.002267</td>\n",
       "      <td>0.017229</td>\n",
       "      <td>0.016089</td>\n",
       "      <td>0.050201</td>\n",
       "      <td>0.059224</td>\n",
       "      <td>0.902713</td>\n",
       "      <td>0.999866</td>\n",
       "      <td>0.018198</td>\n",
       "      <td>0.018377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bpackets</th>\n",
       "      <td>0.004011</td>\n",
       "      <td>0.924622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.156780</td>\n",
       "      <td>0.997268</td>\n",
       "      <td>-0.017667</td>\n",
       "      <td>0.006912</td>\n",
       "      <td>0.018170</td>\n",
       "      <td>0.086886</td>\n",
       "      <td>-0.016104</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000922</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>0.016230</td>\n",
       "      <td>-0.000493</td>\n",
       "      <td>0.048190</td>\n",
       "      <td>0.058435</td>\n",
       "      <td>0.997580</td>\n",
       "      <td>0.924746</td>\n",
       "      <td>0.015124</td>\n",
       "      <td>0.019430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_fpktl</th>\n",
       "      <td>0.001673</td>\n",
       "      <td>0.425756</td>\n",
       "      <td>0.156780</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090082</td>\n",
       "      <td>-0.003099</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>0.021278</td>\n",
       "      <td>0.022088</td>\n",
       "      <td>0.022409</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.009896</td>\n",
       "      <td>0.001657</td>\n",
       "      <td>0.013283</td>\n",
       "      <td>0.015991</td>\n",
       "      <td>0.088422</td>\n",
       "      <td>0.425789</td>\n",
       "      <td>0.005477</td>\n",
       "      <td>0.000679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bpktl</th>\n",
       "      <td>0.003518</td>\n",
       "      <td>0.904007</td>\n",
       "      <td>0.997268</td>\n",
       "      <td>0.090082</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014926</td>\n",
       "      <td>0.005966</td>\n",
       "      <td>0.012560</td>\n",
       "      <td>0.079905</td>\n",
       "      <td>-0.017328</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000812</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.014336</td>\n",
       "      <td>-0.000293</td>\n",
       "      <td>0.043571</td>\n",
       "      <td>0.053134</td>\n",
       "      <td>0.999616</td>\n",
       "      <td>0.904129</td>\n",
       "      <td>0.012139</td>\n",
       "      <td>0.019838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <td>0.029712</td>\n",
       "      <td>0.059224</td>\n",
       "      <td>0.058435</td>\n",
       "      <td>0.015991</td>\n",
       "      <td>0.053134</td>\n",
       "      <td>-0.268444</td>\n",
       "      <td>0.038319</td>\n",
       "      <td>0.429893</td>\n",
       "      <td>0.593143</td>\n",
       "      <td>-0.030004</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026959</td>\n",
       "      <td>0.029512</td>\n",
       "      <td>0.097316</td>\n",
       "      <td>-0.052507</td>\n",
       "      <td>0.811204</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.056761</td>\n",
       "      <td>0.059242</td>\n",
       "      <td>0.333701</td>\n",
       "      <td>0.069405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RRT_samples_clnt</th>\n",
       "      <td>0.003785</td>\n",
       "      <td>0.902713</td>\n",
       "      <td>0.997580</td>\n",
       "      <td>0.088422</td>\n",
       "      <td>0.999616</td>\n",
       "      <td>-0.016659</td>\n",
       "      <td>0.006156</td>\n",
       "      <td>0.015727</td>\n",
       "      <td>0.084280</td>\n",
       "      <td>-0.017595</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000893</td>\n",
       "      <td>0.001560</td>\n",
       "      <td>0.015200</td>\n",
       "      <td>-0.000437</td>\n",
       "      <td>0.046784</td>\n",
       "      <td>0.056761</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.902834</td>\n",
       "      <td>0.014299</td>\n",
       "      <td>0.019679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Act_data_pkt_forward</th>\n",
       "      <td>0.004838</td>\n",
       "      <td>0.999866</td>\n",
       "      <td>0.924746</td>\n",
       "      <td>0.425789</td>\n",
       "      <td>0.904129</td>\n",
       "      <td>-0.018947</td>\n",
       "      <td>0.005264</td>\n",
       "      <td>0.024705</td>\n",
       "      <td>0.086278</td>\n",
       "      <td>-0.007893</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>0.002269</td>\n",
       "      <td>0.017233</td>\n",
       "      <td>0.000734</td>\n",
       "      <td>0.050220</td>\n",
       "      <td>0.059242</td>\n",
       "      <td>0.902834</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018229</td>\n",
       "      <td>0.018391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <td>0.082955</td>\n",
       "      <td>0.018198</td>\n",
       "      <td>0.015124</td>\n",
       "      <td>0.005477</td>\n",
       "      <td>0.012139</td>\n",
       "      <td>-0.686154</td>\n",
       "      <td>-0.189824</td>\n",
       "      <td>-0.074763</td>\n",
       "      <td>0.217989</td>\n",
       "      <td>-0.524024</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077943</td>\n",
       "      <td>0.079324</td>\n",
       "      <td>0.048803</td>\n",
       "      <td>0.052177</td>\n",
       "      <td>0.394743</td>\n",
       "      <td>0.333701</td>\n",
       "      <td>0.014299</td>\n",
       "      <td>0.018229</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.258352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calss</th>\n",
       "      <td>0.067066</td>\n",
       "      <td>0.018377</td>\n",
       "      <td>0.019430</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>0.019838</td>\n",
       "      <td>-0.271343</td>\n",
       "      <td>0.027032</td>\n",
       "      <td>-0.040019</td>\n",
       "      <td>0.073212</td>\n",
       "      <td>-0.211892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066348</td>\n",
       "      <td>0.066965</td>\n",
       "      <td>0.022790</td>\n",
       "      <td>0.018300</td>\n",
       "      <td>0.129425</td>\n",
       "      <td>0.069405</td>\n",
       "      <td>0.019679</td>\n",
       "      <td>0.018391</td>\n",
       "      <td>0.258352</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         duration  total_fpackets  total_bpackets  \\\n",
       "duration                 1.000000        0.004837        0.004011   \n",
       "total_fpackets           0.004837        1.000000        0.924622   \n",
       "total_bpackets           0.004011        0.924622        1.000000   \n",
       "total_fpktl              0.001673        0.425756        0.156780   \n",
       "total_bpktl              0.003518        0.904007        0.997268   \n",
       "...                           ...             ...             ...   \n",
       "Init_Win_bytes_backward  0.029712        0.059224        0.058435   \n",
       "RRT_samples_clnt         0.003785        0.902713        0.997580   \n",
       "Act_data_pkt_forward     0.004838        0.999866        0.924746   \n",
       "min_seg_size_forward     0.082955        0.018198        0.015124   \n",
       "calss                    0.067066        0.018377        0.019430   \n",
       "\n",
       "                         total_fpktl  total_bpktl  min_fpktl  min_bpktl  \\\n",
       "duration                    0.001673     0.003518  -0.064100  -0.027231   \n",
       "total_fpackets              0.425756     0.904007  -0.018958   0.005252   \n",
       "total_bpackets              0.156780     0.997268  -0.017667   0.006912   \n",
       "total_fpktl                 1.000000     0.090082  -0.003099   0.000803   \n",
       "total_bpktl                 0.090082     1.000000  -0.014926   0.005966   \n",
       "...                              ...          ...        ...        ...   \n",
       "Init_Win_bytes_backward     0.015991     0.053134  -0.268444   0.038319   \n",
       "RRT_samples_clnt            0.088422     0.999616  -0.016659   0.006156   \n",
       "Act_data_pkt_forward        0.425789     0.904129  -0.018947   0.005264   \n",
       "min_seg_size_forward        0.005477     0.012139  -0.686154  -0.189824   \n",
       "calss                       0.000679     0.019838  -0.271343   0.027032   \n",
       "\n",
       "                         max_fpktl  max_bpktl  mean_fpktl  ...  mean_idle  \\\n",
       "duration                  0.008761   0.042925   -0.043746  ...   0.998901   \n",
       "total_fpackets            0.024685   0.086255   -0.007910  ...   0.001614   \n",
       "total_bpackets            0.018170   0.086886   -0.016104  ...   0.000922   \n",
       "total_fpktl               0.021278   0.022088    0.022409  ...   0.000335   \n",
       "total_bpktl               0.012560   0.079905   -0.017328  ...   0.000812   \n",
       "...                            ...        ...         ...  ...        ...   \n",
       "Init_Win_bytes_backward   0.429893   0.593143   -0.030004  ...   0.026959   \n",
       "RRT_samples_clnt          0.015727   0.084280   -0.017595  ...   0.000893   \n",
       "Act_data_pkt_forward      0.024705   0.086278   -0.007893  ...   0.001617   \n",
       "min_seg_size_forward     -0.074763   0.217989   -0.524024  ...   0.077943   \n",
       "calss                    -0.040019   0.073212   -0.211892  ...   0.066348   \n",
       "\n",
       "                         max_idle  std_idle    FFNEPD  Init_Win_bytes_forward  \\\n",
       "duration                 0.999458  0.047582  0.016532                0.027610   \n",
       "total_fpackets           0.002267  0.017229  0.016089                0.050201   \n",
       "total_bpackets           0.001617  0.016230 -0.000493                0.048190   \n",
       "total_fpktl              0.000609  0.009896  0.001657                0.013283   \n",
       "total_bpktl              0.001452  0.014336 -0.000293                0.043571   \n",
       "...                           ...       ...       ...                     ...   \n",
       "Init_Win_bytes_backward  0.029512  0.097316 -0.052507                0.811204   \n",
       "RRT_samples_clnt         0.001560  0.015200 -0.000437                0.046784   \n",
       "Act_data_pkt_forward     0.002269  0.017233  0.000734                0.050220   \n",
       "min_seg_size_forward     0.079324  0.048803  0.052177                0.394743   \n",
       "calss                    0.066965  0.022790  0.018300                0.129425   \n",
       "\n",
       "                         Init_Win_bytes_backward  RRT_samples_clnt  \\\n",
       "duration                                0.029712          0.003785   \n",
       "total_fpackets                          0.059224          0.902713   \n",
       "total_bpackets                          0.058435          0.997580   \n",
       "total_fpktl                             0.015991          0.088422   \n",
       "total_bpktl                             0.053134          0.999616   \n",
       "...                                          ...               ...   \n",
       "Init_Win_bytes_backward                 1.000000          0.056761   \n",
       "RRT_samples_clnt                        0.056761          1.000000   \n",
       "Act_data_pkt_forward                    0.059242          0.902834   \n",
       "min_seg_size_forward                    0.333701          0.014299   \n",
       "calss                                   0.069405          0.019679   \n",
       "\n",
       "                         Act_data_pkt_forward  min_seg_size_forward     calss  \n",
       "duration                             0.004838              0.082955  0.067066  \n",
       "total_fpackets                       0.999866              0.018198  0.018377  \n",
       "total_bpackets                       0.924746              0.015124  0.019430  \n",
       "total_fpktl                          0.425789              0.005477  0.000679  \n",
       "total_bpktl                          0.904129              0.012139  0.019838  \n",
       "...                                       ...                   ...       ...  \n",
       "Init_Win_bytes_backward              0.059242              0.333701  0.069405  \n",
       "RRT_samples_clnt                     0.902834              0.014299  0.019679  \n",
       "Act_data_pkt_forward                 1.000000              0.018229  0.018391  \n",
       "min_seg_size_forward                 0.018229              1.000000  0.258352  \n",
       "calss                                0.018391              0.258352  1.000000  \n",
       "\n",
       "[80 rows x 80 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>total_fpackets</th>\n",
       "      <th>total_bpackets</th>\n",
       "      <th>total_fpktl</th>\n",
       "      <th>total_bpktl</th>\n",
       "      <th>min_fpktl</th>\n",
       "      <th>min_bpktl</th>\n",
       "      <th>max_fpktl</th>\n",
       "      <th>max_bpktl</th>\n",
       "      <th>mean_fpktl</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_idle</th>\n",
       "      <th>max_idle</th>\n",
       "      <th>std_idle</th>\n",
       "      <th>FFNEPD</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <th>RRT_samples_clnt</th>\n",
       "      <th>Act_data_pkt_forward</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <th>calss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004837</td>\n",
       "      <td>0.004011</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>0.003518</td>\n",
       "      <td>-0.064100</td>\n",
       "      <td>-0.027231</td>\n",
       "      <td>0.008761</td>\n",
       "      <td>0.042925</td>\n",
       "      <td>-0.043746</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998901</td>\n",
       "      <td>0.999458</td>\n",
       "      <td>0.047582</td>\n",
       "      <td>0.016532</td>\n",
       "      <td>0.027610</td>\n",
       "      <td>0.029712</td>\n",
       "      <td>0.003785</td>\n",
       "      <td>0.004838</td>\n",
       "      <td>0.082955</td>\n",
       "      <td>0.067066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_bpktl</th>\n",
       "      <td>0.042925</td>\n",
       "      <td>0.086255</td>\n",
       "      <td>0.086886</td>\n",
       "      <td>0.022088</td>\n",
       "      <td>0.079905</td>\n",
       "      <td>-0.277317</td>\n",
       "      <td>0.275923</td>\n",
       "      <td>0.492194</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018358</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035413</td>\n",
       "      <td>0.038732</td>\n",
       "      <td>0.127548</td>\n",
       "      <td>-0.044916</td>\n",
       "      <td>0.586742</td>\n",
       "      <td>0.593143</td>\n",
       "      <td>0.084280</td>\n",
       "      <td>0.086278</td>\n",
       "      <td>0.217989</td>\n",
       "      <td>0.073212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_bpktl</th>\n",
       "      <td>0.025117</td>\n",
       "      <td>0.139142</td>\n",
       "      <td>0.151761</td>\n",
       "      <td>0.018954</td>\n",
       "      <td>0.146437</td>\n",
       "      <td>-0.280648</td>\n",
       "      <td>0.465208</td>\n",
       "      <td>0.342392</td>\n",
       "      <td>0.895712</td>\n",
       "      <td>-0.096195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018533</td>\n",
       "      <td>0.021519</td>\n",
       "      <td>0.114949</td>\n",
       "      <td>-0.045442</td>\n",
       "      <td>0.452735</td>\n",
       "      <td>0.478823</td>\n",
       "      <td>0.150252</td>\n",
       "      <td>0.139172</td>\n",
       "      <td>0.122226</td>\n",
       "      <td>0.064753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fpktl</th>\n",
       "      <td>0.039350</td>\n",
       "      <td>0.010172</td>\n",
       "      <td>0.002331</td>\n",
       "      <td>0.011416</td>\n",
       "      <td>-0.003162</td>\n",
       "      <td>-0.245792</td>\n",
       "      <td>0.052877</td>\n",
       "      <td>0.817873</td>\n",
       "      <td>0.564243</td>\n",
       "      <td>0.259588</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031418</td>\n",
       "      <td>0.033978</td>\n",
       "      <td>0.096844</td>\n",
       "      <td>-0.020885</td>\n",
       "      <td>0.558249</td>\n",
       "      <td>0.532900</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.010182</td>\n",
       "      <td>0.301237</td>\n",
       "      <td>0.123758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_bpktl</th>\n",
       "      <td>0.048743</td>\n",
       "      <td>0.020324</td>\n",
       "      <td>0.014005</td>\n",
       "      <td>0.007763</td>\n",
       "      <td>0.007768</td>\n",
       "      <td>-0.225143</td>\n",
       "      <td>0.035371</td>\n",
       "      <td>0.534532</td>\n",
       "      <td>0.941626</td>\n",
       "      <td>0.051756</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042562</td>\n",
       "      <td>0.045450</td>\n",
       "      <td>0.113854</td>\n",
       "      <td>-0.033991</td>\n",
       "      <td>0.594790</td>\n",
       "      <td>0.577954</td>\n",
       "      <td>0.012050</td>\n",
       "      <td>0.020335</td>\n",
       "      <td>0.276055</td>\n",
       "      <td>0.072953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_fiat</th>\n",
       "      <td>0.943898</td>\n",
       "      <td>0.002190</td>\n",
       "      <td>0.001718</td>\n",
       "      <td>0.000708</td>\n",
       "      <td>0.001546</td>\n",
       "      <td>-0.049879</td>\n",
       "      <td>-0.031218</td>\n",
       "      <td>-0.029233</td>\n",
       "      <td>-0.005961</td>\n",
       "      <td>-0.048869</td>\n",
       "      <td>...</td>\n",
       "      <td>0.943668</td>\n",
       "      <td>0.943339</td>\n",
       "      <td>0.015254</td>\n",
       "      <td>0.020302</td>\n",
       "      <td>-0.011637</td>\n",
       "      <td>-0.016421</td>\n",
       "      <td>0.001615</td>\n",
       "      <td>0.002190</td>\n",
       "      <td>0.065124</td>\n",
       "      <td>0.064770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_fiat</th>\n",
       "      <td>0.841692</td>\n",
       "      <td>-0.001975</td>\n",
       "      <td>-0.002172</td>\n",
       "      <td>-0.000746</td>\n",
       "      <td>-0.001820</td>\n",
       "      <td>-0.040046</td>\n",
       "      <td>-0.036619</td>\n",
       "      <td>-0.052289</td>\n",
       "      <td>-0.035963</td>\n",
       "      <td>-0.053811</td>\n",
       "      <td>...</td>\n",
       "      <td>0.843330</td>\n",
       "      <td>0.842512</td>\n",
       "      <td>-0.005389</td>\n",
       "      <td>0.015316</td>\n",
       "      <td>-0.027350</td>\n",
       "      <td>-0.033944</td>\n",
       "      <td>-0.002037</td>\n",
       "      <td>-0.001974</td>\n",
       "      <td>0.051853</td>\n",
       "      <td>0.074491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_fiat</th>\n",
       "      <td>0.943438</td>\n",
       "      <td>-0.000420</td>\n",
       "      <td>-0.000714</td>\n",
       "      <td>-0.000389</td>\n",
       "      <td>-0.000548</td>\n",
       "      <td>-0.046977</td>\n",
       "      <td>-0.032630</td>\n",
       "      <td>-0.032606</td>\n",
       "      <td>-0.009878</td>\n",
       "      <td>-0.049032</td>\n",
       "      <td>...</td>\n",
       "      <td>0.944412</td>\n",
       "      <td>0.943971</td>\n",
       "      <td>0.012557</td>\n",
       "      <td>0.017756</td>\n",
       "      <td>-0.012317</td>\n",
       "      <td>-0.016206</td>\n",
       "      <td>-0.000644</td>\n",
       "      <td>-0.000418</td>\n",
       "      <td>0.061285</td>\n",
       "      <td>0.064875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_fiat</th>\n",
       "      <td>0.918036</td>\n",
       "      <td>-0.001970</td>\n",
       "      <td>-0.002241</td>\n",
       "      <td>-0.000728</td>\n",
       "      <td>-0.001907</td>\n",
       "      <td>-0.042455</td>\n",
       "      <td>-0.036056</td>\n",
       "      <td>-0.048712</td>\n",
       "      <td>-0.032412</td>\n",
       "      <td>-0.052892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919653</td>\n",
       "      <td>0.918797</td>\n",
       "      <td>-0.003523</td>\n",
       "      <td>0.016958</td>\n",
       "      <td>-0.025823</td>\n",
       "      <td>-0.032466</td>\n",
       "      <td>-0.002113</td>\n",
       "      <td>-0.001968</td>\n",
       "      <td>0.055151</td>\n",
       "      <td>0.071397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_flowpktl</th>\n",
       "      <td>0.036942</td>\n",
       "      <td>0.087741</td>\n",
       "      <td>0.087307</td>\n",
       "      <td>0.024298</td>\n",
       "      <td>0.080666</td>\n",
       "      <td>-0.262968</td>\n",
       "      <td>0.338513</td>\n",
       "      <td>0.631466</td>\n",
       "      <td>0.911584</td>\n",
       "      <td>0.095976</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030013</td>\n",
       "      <td>0.033100</td>\n",
       "      <td>0.120257</td>\n",
       "      <td>-0.035737</td>\n",
       "      <td>0.580266</td>\n",
       "      <td>0.586355</td>\n",
       "      <td>0.084725</td>\n",
       "      <td>0.087764</td>\n",
       "      <td>0.219346</td>\n",
       "      <td>0.119375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_flowiat</th>\n",
       "      <td>0.841666</td>\n",
       "      <td>-0.001972</td>\n",
       "      <td>-0.002169</td>\n",
       "      <td>-0.000744</td>\n",
       "      <td>-0.001816</td>\n",
       "      <td>-0.039980</td>\n",
       "      <td>-0.036623</td>\n",
       "      <td>-0.052221</td>\n",
       "      <td>-0.035917</td>\n",
       "      <td>-0.053744</td>\n",
       "      <td>...</td>\n",
       "      <td>0.843311</td>\n",
       "      <td>0.842493</td>\n",
       "      <td>-0.005388</td>\n",
       "      <td>0.015323</td>\n",
       "      <td>-0.027316</td>\n",
       "      <td>-0.033903</td>\n",
       "      <td>-0.002033</td>\n",
       "      <td>-0.001971</td>\n",
       "      <td>0.051763</td>\n",
       "      <td>0.074452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_flowiat</th>\n",
       "      <td>0.999457</td>\n",
       "      <td>0.002273</td>\n",
       "      <td>0.001623</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.001458</td>\n",
       "      <td>-0.061004</td>\n",
       "      <td>-0.028758</td>\n",
       "      <td>0.005240</td>\n",
       "      <td>0.038796</td>\n",
       "      <td>-0.043865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999602</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.043309</td>\n",
       "      <td>0.013884</td>\n",
       "      <td>0.026609</td>\n",
       "      <td>0.029591</td>\n",
       "      <td>0.001566</td>\n",
       "      <td>0.002276</td>\n",
       "      <td>0.079417</td>\n",
       "      <td>0.066992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_flowiat</th>\n",
       "      <td>0.949299</td>\n",
       "      <td>-0.002050</td>\n",
       "      <td>-0.002292</td>\n",
       "      <td>-0.000748</td>\n",
       "      <td>-0.001972</td>\n",
       "      <td>-0.044032</td>\n",
       "      <td>-0.035728</td>\n",
       "      <td>-0.048943</td>\n",
       "      <td>-0.031180</td>\n",
       "      <td>-0.054610</td>\n",
       "      <td>...</td>\n",
       "      <td>0.950940</td>\n",
       "      <td>0.950117</td>\n",
       "      <td>-0.002380</td>\n",
       "      <td>0.016349</td>\n",
       "      <td>-0.024163</td>\n",
       "      <td>-0.030175</td>\n",
       "      <td>-0.002169</td>\n",
       "      <td>-0.002048</td>\n",
       "      <td>0.056882</td>\n",
       "      <td>0.072549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flow_fin</th>\n",
       "      <td>0.068154</td>\n",
       "      <td>0.005461</td>\n",
       "      <td>0.004894</td>\n",
       "      <td>-0.001464</td>\n",
       "      <td>0.005112</td>\n",
       "      <td>-0.408271</td>\n",
       "      <td>-0.155843</td>\n",
       "      <td>-0.126824</td>\n",
       "      <td>0.035163</td>\n",
       "      <td>-0.356749</td>\n",
       "      <td>...</td>\n",
       "      <td>0.071426</td>\n",
       "      <td>0.071654</td>\n",
       "      <td>0.017388</td>\n",
       "      <td>0.048678</td>\n",
       "      <td>0.238759</td>\n",
       "      <td>0.204597</td>\n",
       "      <td>0.005338</td>\n",
       "      <td>0.005479</td>\n",
       "      <td>0.551140</td>\n",
       "      <td>0.286175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flow_syn</th>\n",
       "      <td>0.020298</td>\n",
       "      <td>0.045730</td>\n",
       "      <td>0.043305</td>\n",
       "      <td>0.012057</td>\n",
       "      <td>0.039253</td>\n",
       "      <td>-0.268203</td>\n",
       "      <td>0.010317</td>\n",
       "      <td>0.388110</td>\n",
       "      <td>0.537187</td>\n",
       "      <td>-0.043379</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014644</td>\n",
       "      <td>0.018113</td>\n",
       "      <td>0.112752</td>\n",
       "      <td>0.012415</td>\n",
       "      <td>0.890695</td>\n",
       "      <td>0.729790</td>\n",
       "      <td>0.042069</td>\n",
       "      <td>0.045747</td>\n",
       "      <td>0.370962</td>\n",
       "      <td>0.115044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flow_rst</th>\n",
       "      <td>-0.003951</td>\n",
       "      <td>-0.004097</td>\n",
       "      <td>-0.004343</td>\n",
       "      <td>-0.001550</td>\n",
       "      <td>-0.003732</td>\n",
       "      <td>-0.098533</td>\n",
       "      <td>-0.063549</td>\n",
       "      <td>-0.106686</td>\n",
       "      <td>-0.040593</td>\n",
       "      <td>-0.121735</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002382</td>\n",
       "      <td>-0.002685</td>\n",
       "      <td>-0.009004</td>\n",
       "      <td>0.028905</td>\n",
       "      <td>-0.067341</td>\n",
       "      <td>-0.064202</td>\n",
       "      <td>-0.004112</td>\n",
       "      <td>-0.004092</td>\n",
       "      <td>0.064160</td>\n",
       "      <td>0.058715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bVarianceDataBytes</th>\n",
       "      <td>0.042755</td>\n",
       "      <td>0.012731</td>\n",
       "      <td>0.006942</td>\n",
       "      <td>0.004947</td>\n",
       "      <td>0.002038</td>\n",
       "      <td>-0.184458</td>\n",
       "      <td>0.027897</td>\n",
       "      <td>0.428040</td>\n",
       "      <td>0.900543</td>\n",
       "      <td>0.019958</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039000</td>\n",
       "      <td>0.041333</td>\n",
       "      <td>0.094613</td>\n",
       "      <td>-0.030463</td>\n",
       "      <td>0.516196</td>\n",
       "      <td>0.507734</td>\n",
       "      <td>0.005591</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>0.231281</td>\n",
       "      <td>0.056086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bAvgSegmentSize</th>\n",
       "      <td>0.025105</td>\n",
       "      <td>0.139151</td>\n",
       "      <td>0.151778</td>\n",
       "      <td>0.018949</td>\n",
       "      <td>0.146457</td>\n",
       "      <td>-0.280510</td>\n",
       "      <td>0.465396</td>\n",
       "      <td>0.342137</td>\n",
       "      <td>0.895560</td>\n",
       "      <td>-0.096229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018527</td>\n",
       "      <td>0.021512</td>\n",
       "      <td>0.114916</td>\n",
       "      <td>-0.045430</td>\n",
       "      <td>0.452305</td>\n",
       "      <td>0.478400</td>\n",
       "      <td>0.150270</td>\n",
       "      <td>0.139180</td>\n",
       "      <td>0.121997</td>\n",
       "      <td>0.064755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_active</th>\n",
       "      <td>0.997972</td>\n",
       "      <td>0.002195</td>\n",
       "      <td>0.001495</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.001410</td>\n",
       "      <td>-0.058989</td>\n",
       "      <td>-0.028861</td>\n",
       "      <td>0.001318</td>\n",
       "      <td>0.033057</td>\n",
       "      <td>-0.044135</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999746</td>\n",
       "      <td>0.998771</td>\n",
       "      <td>-0.005581</td>\n",
       "      <td>0.012340</td>\n",
       "      <td>0.022190</td>\n",
       "      <td>0.025199</td>\n",
       "      <td>0.001474</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.077047</td>\n",
       "      <td>0.066001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_active</th>\n",
       "      <td>0.998911</td>\n",
       "      <td>0.002580</td>\n",
       "      <td>0.001852</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.001724</td>\n",
       "      <td>-0.059924</td>\n",
       "      <td>-0.028800</td>\n",
       "      <td>0.003287</td>\n",
       "      <td>0.035932</td>\n",
       "      <td>-0.043954</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999603</td>\n",
       "      <td>0.016879</td>\n",
       "      <td>0.012854</td>\n",
       "      <td>0.024362</td>\n",
       "      <td>0.027367</td>\n",
       "      <td>0.001808</td>\n",
       "      <td>0.002582</td>\n",
       "      <td>0.078161</td>\n",
       "      <td>0.066552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_active</th>\n",
       "      <td>0.999465</td>\n",
       "      <td>0.003261</td>\n",
       "      <td>0.002592</td>\n",
       "      <td>0.000919</td>\n",
       "      <td>0.002409</td>\n",
       "      <td>-0.061083</td>\n",
       "      <td>-0.028773</td>\n",
       "      <td>0.005583</td>\n",
       "      <td>0.039286</td>\n",
       "      <td>-0.043762</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999596</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.043430</td>\n",
       "      <td>0.013858</td>\n",
       "      <td>0.027031</td>\n",
       "      <td>0.029961</td>\n",
       "      <td>0.002520</td>\n",
       "      <td>0.003264</td>\n",
       "      <td>0.079542</td>\n",
       "      <td>0.067178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_idle</th>\n",
       "      <td>0.997952</td>\n",
       "      <td>0.001258</td>\n",
       "      <td>0.000610</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>-0.058793</td>\n",
       "      <td>-0.028867</td>\n",
       "      <td>0.001028</td>\n",
       "      <td>0.032544</td>\n",
       "      <td>-0.044117</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999743</td>\n",
       "      <td>0.998762</td>\n",
       "      <td>-0.005776</td>\n",
       "      <td>0.012279</td>\n",
       "      <td>0.021657</td>\n",
       "      <td>0.024794</td>\n",
       "      <td>0.000605</td>\n",
       "      <td>0.001261</td>\n",
       "      <td>0.076794</td>\n",
       "      <td>0.065804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_idle</th>\n",
       "      <td>0.998901</td>\n",
       "      <td>0.001614</td>\n",
       "      <td>0.000922</td>\n",
       "      <td>0.000335</td>\n",
       "      <td>0.000812</td>\n",
       "      <td>-0.059748</td>\n",
       "      <td>-0.028819</td>\n",
       "      <td>0.002983</td>\n",
       "      <td>0.035413</td>\n",
       "      <td>-0.043962</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999601</td>\n",
       "      <td>0.016744</td>\n",
       "      <td>0.012852</td>\n",
       "      <td>0.023865</td>\n",
       "      <td>0.026959</td>\n",
       "      <td>0.000893</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>0.077943</td>\n",
       "      <td>0.066348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_idle</th>\n",
       "      <td>0.999458</td>\n",
       "      <td>0.002267</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>-0.060904</td>\n",
       "      <td>-0.028800</td>\n",
       "      <td>0.005244</td>\n",
       "      <td>0.038732</td>\n",
       "      <td>-0.043786</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999601</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.043323</td>\n",
       "      <td>0.013880</td>\n",
       "      <td>0.026525</td>\n",
       "      <td>0.029512</td>\n",
       "      <td>0.001560</td>\n",
       "      <td>0.002269</td>\n",
       "      <td>0.079324</td>\n",
       "      <td>0.066965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <td>0.027610</td>\n",
       "      <td>0.050201</td>\n",
       "      <td>0.048190</td>\n",
       "      <td>0.013283</td>\n",
       "      <td>0.043571</td>\n",
       "      <td>-0.323350</td>\n",
       "      <td>0.007284</td>\n",
       "      <td>0.409109</td>\n",
       "      <td>0.586742</td>\n",
       "      <td>-0.075007</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023865</td>\n",
       "      <td>0.026525</td>\n",
       "      <td>0.095880</td>\n",
       "      <td>-0.021448</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.811204</td>\n",
       "      <td>0.046784</td>\n",
       "      <td>0.050220</td>\n",
       "      <td>0.394743</td>\n",
       "      <td>0.129425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <td>0.029712</td>\n",
       "      <td>0.059224</td>\n",
       "      <td>0.058435</td>\n",
       "      <td>0.015991</td>\n",
       "      <td>0.053134</td>\n",
       "      <td>-0.268444</td>\n",
       "      <td>0.038319</td>\n",
       "      <td>0.429893</td>\n",
       "      <td>0.593143</td>\n",
       "      <td>-0.030004</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026959</td>\n",
       "      <td>0.029512</td>\n",
       "      <td>0.097316</td>\n",
       "      <td>-0.052507</td>\n",
       "      <td>0.811204</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.056761</td>\n",
       "      <td>0.059242</td>\n",
       "      <td>0.333701</td>\n",
       "      <td>0.069405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <td>0.082955</td>\n",
       "      <td>0.018198</td>\n",
       "      <td>0.015124</td>\n",
       "      <td>0.005477</td>\n",
       "      <td>0.012139</td>\n",
       "      <td>-0.686154</td>\n",
       "      <td>-0.189824</td>\n",
       "      <td>-0.074763</td>\n",
       "      <td>0.217989</td>\n",
       "      <td>-0.524024</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077943</td>\n",
       "      <td>0.079324</td>\n",
       "      <td>0.048803</td>\n",
       "      <td>0.052177</td>\n",
       "      <td>0.394743</td>\n",
       "      <td>0.333701</td>\n",
       "      <td>0.014299</td>\n",
       "      <td>0.018229</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.258352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calss</th>\n",
       "      <td>0.067066</td>\n",
       "      <td>0.018377</td>\n",
       "      <td>0.019430</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>0.019838</td>\n",
       "      <td>-0.271343</td>\n",
       "      <td>0.027032</td>\n",
       "      <td>-0.040019</td>\n",
       "      <td>0.073212</td>\n",
       "      <td>-0.211892</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066348</td>\n",
       "      <td>0.066965</td>\n",
       "      <td>0.022790</td>\n",
       "      <td>0.018300</td>\n",
       "      <td>0.129425</td>\n",
       "      <td>0.069405</td>\n",
       "      <td>0.019679</td>\n",
       "      <td>0.018391</td>\n",
       "      <td>0.258352</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         duration  total_fpackets  total_bpackets  \\\n",
       "duration                 1.000000        0.004837        0.004011   \n",
       "max_bpktl                0.042925        0.086255        0.086886   \n",
       "mean_bpktl               0.025117        0.139142        0.151761   \n",
       "std_fpktl                0.039350        0.010172        0.002331   \n",
       "std_bpktl                0.048743        0.020324        0.014005   \n",
       "total_fiat               0.943898        0.002190        0.001718   \n",
       "min_fiat                 0.841692       -0.001975       -0.002172   \n",
       "max_fiat                 0.943438       -0.000420       -0.000714   \n",
       "mean_fiat                0.918036       -0.001970       -0.002241   \n",
       "std_flowpktl             0.036942        0.087741        0.087307   \n",
       "min_flowiat              0.841666       -0.001972       -0.002169   \n",
       "max_flowiat              0.999457        0.002273        0.001623   \n",
       "mean_flowiat             0.949299       -0.002050       -0.002292   \n",
       "flow_fin                 0.068154        0.005461        0.004894   \n",
       "flow_syn                 0.020298        0.045730        0.043305   \n",
       "flow_rst                -0.003951       -0.004097       -0.004343   \n",
       "bVarianceDataBytes       0.042755        0.012731        0.006942   \n",
       "bAvgSegmentSize          0.025105        0.139151        0.151778   \n",
       "min_active               0.997972        0.002195        0.001495   \n",
       "mean_active              0.998911        0.002580        0.001852   \n",
       "max_active               0.999465        0.003261        0.002592   \n",
       "min_idle                 0.997952        0.001258        0.000610   \n",
       "mean_idle                0.998901        0.001614        0.000922   \n",
       "max_idle                 0.999458        0.002267        0.001617   \n",
       "Init_Win_bytes_forward   0.027610        0.050201        0.048190   \n",
       "Init_Win_bytes_backward  0.029712        0.059224        0.058435   \n",
       "min_seg_size_forward     0.082955        0.018198        0.015124   \n",
       "calss                    0.067066        0.018377        0.019430   \n",
       "\n",
       "                         total_fpktl  total_bpktl  min_fpktl  min_bpktl  \\\n",
       "duration                    0.001673     0.003518  -0.064100  -0.027231   \n",
       "max_bpktl                   0.022088     0.079905  -0.277317   0.275923   \n",
       "mean_bpktl                  0.018954     0.146437  -0.280648   0.465208   \n",
       "std_fpktl                   0.011416    -0.003162  -0.245792   0.052877   \n",
       "std_bpktl                   0.007763     0.007768  -0.225143   0.035371   \n",
       "total_fiat                  0.000708     0.001546  -0.049879  -0.031218   \n",
       "min_fiat                   -0.000746    -0.001820  -0.040046  -0.036619   \n",
       "max_fiat                   -0.000389    -0.000548  -0.046977  -0.032630   \n",
       "mean_fiat                  -0.000728    -0.001907  -0.042455  -0.036056   \n",
       "std_flowpktl                0.024298     0.080666  -0.262968   0.338513   \n",
       "min_flowiat                -0.000744    -0.001816  -0.039980  -0.036623   \n",
       "max_flowiat                 0.000611     0.001458  -0.061004  -0.028758   \n",
       "mean_flowiat               -0.000748    -0.001972  -0.044032  -0.035728   \n",
       "flow_fin                   -0.001464     0.005112  -0.408271  -0.155843   \n",
       "flow_syn                    0.012057     0.039253  -0.268203   0.010317   \n",
       "flow_rst                   -0.001550    -0.003732  -0.098533  -0.063549   \n",
       "bVarianceDataBytes          0.004947     0.002038  -0.184458   0.027897   \n",
       "bAvgSegmentSize             0.018949     0.146457  -0.280510   0.465396   \n",
       "min_active                  0.000434     0.001410  -0.058989  -0.028861   \n",
       "mean_active                 0.000650     0.001724  -0.059924  -0.028800   \n",
       "max_active                  0.000919     0.002409  -0.061083  -0.028773   \n",
       "min_idle                    0.000113     0.000544  -0.058793  -0.028867   \n",
       "mean_idle                   0.000335     0.000812  -0.059748  -0.028819   \n",
       "max_idle                    0.000609     0.001452  -0.060904  -0.028800   \n",
       "Init_Win_bytes_forward      0.013283     0.043571  -0.323350   0.007284   \n",
       "Init_Win_bytes_backward     0.015991     0.053134  -0.268444   0.038319   \n",
       "min_seg_size_forward        0.005477     0.012139  -0.686154  -0.189824   \n",
       "calss                       0.000679     0.019838  -0.271343   0.027032   \n",
       "\n",
       "                         max_fpktl  max_bpktl  mean_fpktl  ...  mean_idle  \\\n",
       "duration                  0.008761   0.042925   -0.043746  ...   0.998901   \n",
       "max_bpktl                 0.492194   1.000000   -0.018358  ...   0.035413   \n",
       "mean_bpktl                0.342392   0.895712   -0.096195  ...   0.018533   \n",
       "std_fpktl                 0.817873   0.564243    0.259588  ...   0.031418   \n",
       "std_bpktl                 0.534532   0.941626    0.051756  ...   0.042562   \n",
       "total_fiat               -0.029233  -0.005961   -0.048869  ...   0.943668   \n",
       "min_fiat                 -0.052289  -0.035963   -0.053811  ...   0.843330   \n",
       "max_fiat                 -0.032606  -0.009878   -0.049032  ...   0.944412   \n",
       "mean_fiat                -0.048712  -0.032412   -0.052892  ...   0.919653   \n",
       "std_flowpktl              0.631466   0.911584    0.095976  ...   0.030013   \n",
       "min_flowiat              -0.052221  -0.035917   -0.053744  ...   0.843311   \n",
       "max_flowiat               0.005240   0.038796   -0.043865  ...   0.999602   \n",
       "mean_flowiat             -0.048943  -0.031180   -0.054610  ...   0.950940   \n",
       "flow_fin                 -0.126824   0.035163   -0.356749  ...   0.071426   \n",
       "flow_syn                  0.388110   0.537187   -0.043379  ...   0.014644   \n",
       "flow_rst                 -0.106686  -0.040593   -0.121735  ...  -0.002382   \n",
       "bVarianceDataBytes        0.428040   0.900543    0.019958  ...   0.039000   \n",
       "bAvgSegmentSize           0.342137   0.895560   -0.096229  ...   0.018527   \n",
       "min_active                0.001318   0.033057   -0.044135  ...   0.999746   \n",
       "mean_active               0.003287   0.035932   -0.043954  ...   0.999998   \n",
       "max_active                0.005583   0.039286   -0.043762  ...   0.999596   \n",
       "min_idle                  0.001028   0.032544   -0.044117  ...   0.999743   \n",
       "mean_idle                 0.002983   0.035413   -0.043962  ...   1.000000   \n",
       "max_idle                  0.005244   0.038732   -0.043786  ...   0.999601   \n",
       "Init_Win_bytes_forward    0.409109   0.586742   -0.075007  ...   0.023865   \n",
       "Init_Win_bytes_backward   0.429893   0.593143   -0.030004  ...   0.026959   \n",
       "min_seg_size_forward     -0.074763   0.217989   -0.524024  ...   0.077943   \n",
       "calss                    -0.040019   0.073212   -0.211892  ...   0.066348   \n",
       "\n",
       "                         max_idle  std_idle    FFNEPD  Init_Win_bytes_forward  \\\n",
       "duration                 0.999458  0.047582  0.016532                0.027610   \n",
       "max_bpktl                0.038732  0.127548 -0.044916                0.586742   \n",
       "mean_bpktl               0.021519  0.114949 -0.045442                0.452735   \n",
       "std_fpktl                0.033978  0.096844 -0.020885                0.558249   \n",
       "std_bpktl                0.045450  0.113854 -0.033991                0.594790   \n",
       "total_fiat               0.943339  0.015254  0.020302               -0.011637   \n",
       "min_fiat                 0.842512 -0.005389  0.015316               -0.027350   \n",
       "max_fiat                 0.943971  0.012557  0.017756               -0.012317   \n",
       "mean_fiat                0.918797 -0.003523  0.016958               -0.025823   \n",
       "std_flowpktl             0.033100  0.120257 -0.035737                0.580266   \n",
       "min_flowiat              0.842493 -0.005388  0.015323               -0.027316   \n",
       "max_flowiat              1.000000  0.043309  0.013884                0.026609   \n",
       "mean_flowiat             0.950117 -0.002380  0.016349               -0.024163   \n",
       "flow_fin                 0.071654  0.017388  0.048678                0.238759   \n",
       "flow_syn                 0.018113  0.112752  0.012415                0.890695   \n",
       "flow_rst                -0.002685 -0.009004  0.028905               -0.067341   \n",
       "bVarianceDataBytes       0.041333  0.094613 -0.030463                0.516196   \n",
       "bAvgSegmentSize          0.021512  0.114916 -0.045430                0.452305   \n",
       "min_active               0.998771 -0.005581  0.012340                0.022190   \n",
       "mean_active              0.999603  0.016879  0.012854                0.024362   \n",
       "max_active               0.999998  0.043430  0.013858                0.027031   \n",
       "min_idle                 0.998762 -0.005776  0.012279                0.021657   \n",
       "mean_idle                0.999601  0.016744  0.012852                0.023865   \n",
       "max_idle                 1.000000  0.043323  0.013880                0.026525   \n",
       "Init_Win_bytes_forward   0.026525  0.095880 -0.021448                1.000000   \n",
       "Init_Win_bytes_backward  0.029512  0.097316 -0.052507                0.811204   \n",
       "min_seg_size_forward     0.079324  0.048803  0.052177                0.394743   \n",
       "calss                    0.066965  0.022790  0.018300                0.129425   \n",
       "\n",
       "                         Init_Win_bytes_backward  RRT_samples_clnt  \\\n",
       "duration                                0.029712          0.003785   \n",
       "max_bpktl                               0.593143          0.084280   \n",
       "mean_bpktl                              0.478823          0.150252   \n",
       "std_fpktl                               0.532900          0.000140   \n",
       "std_bpktl                               0.577954          0.012050   \n",
       "total_fiat                             -0.016421          0.001615   \n",
       "min_fiat                               -0.033944         -0.002037   \n",
       "max_fiat                               -0.016206         -0.000644   \n",
       "mean_fiat                              -0.032466         -0.002113   \n",
       "std_flowpktl                            0.586355          0.084725   \n",
       "min_flowiat                            -0.033903         -0.002033   \n",
       "max_flowiat                             0.029591          0.001566   \n",
       "mean_flowiat                           -0.030175         -0.002169   \n",
       "flow_fin                                0.204597          0.005338   \n",
       "flow_syn                                0.729790          0.042069   \n",
       "flow_rst                               -0.064202         -0.004112   \n",
       "bVarianceDataBytes                      0.507734          0.005591   \n",
       "bAvgSegmentSize                         0.478400          0.150270   \n",
       "min_active                              0.025199          0.001474   \n",
       "mean_active                             0.027367          0.001808   \n",
       "max_active                              0.029961          0.002520   \n",
       "min_idle                                0.024794          0.000605   \n",
       "mean_idle                               0.026959          0.000893   \n",
       "max_idle                                0.029512          0.001560   \n",
       "Init_Win_bytes_forward                  0.811204          0.046784   \n",
       "Init_Win_bytes_backward                 1.000000          0.056761   \n",
       "min_seg_size_forward                    0.333701          0.014299   \n",
       "calss                                   0.069405          0.019679   \n",
       "\n",
       "                         Act_data_pkt_forward  min_seg_size_forward     calss  \n",
       "duration                             0.004838              0.082955  0.067066  \n",
       "max_bpktl                            0.086278              0.217989  0.073212  \n",
       "mean_bpktl                           0.139172              0.122226  0.064753  \n",
       "std_fpktl                            0.010182              0.301237  0.123758  \n",
       "std_bpktl                            0.020335              0.276055  0.072953  \n",
       "total_fiat                           0.002190              0.065124  0.064770  \n",
       "min_fiat                            -0.001974              0.051853  0.074491  \n",
       "max_fiat                            -0.000418              0.061285  0.064875  \n",
       "mean_fiat                           -0.001968              0.055151  0.071397  \n",
       "std_flowpktl                         0.087764              0.219346  0.119375  \n",
       "min_flowiat                         -0.001971              0.051763  0.074452  \n",
       "max_flowiat                          0.002276              0.079417  0.066992  \n",
       "mean_flowiat                        -0.002048              0.056882  0.072549  \n",
       "flow_fin                             0.005479              0.551140  0.286175  \n",
       "flow_syn                             0.045747              0.370962  0.115044  \n",
       "flow_rst                            -0.004092              0.064160  0.058715  \n",
       "bVarianceDataBytes                   0.012739              0.231281  0.056086  \n",
       "bAvgSegmentSize                      0.139180              0.121997  0.064755  \n",
       "min_active                           0.002198              0.077047  0.066001  \n",
       "mean_active                          0.002582              0.078161  0.066552  \n",
       "max_active                           0.003264              0.079542  0.067178  \n",
       "min_idle                             0.001261              0.076794  0.065804  \n",
       "mean_idle                            0.001617              0.077943  0.066348  \n",
       "max_idle                             0.002269              0.079324  0.066965  \n",
       "Init_Win_bytes_forward               0.050220              0.394743  0.129425  \n",
       "Init_Win_bytes_backward              0.059242              0.333701  0.069405  \n",
       "min_seg_size_forward                 0.018229              1.000000  0.258352  \n",
       "calss                                0.018391              0.258352  1.000000  \n",
       "\n",
       "[28 rows x 80 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se puede llegar a valorar quedarnos con aquellas que tienen mayor correlación\n",
    "corr_matrix[corr_matrix[\"calss\"] > 0.05]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. División del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividimos el conjunto de datos\n",
    "train_set, val_set, test_set = train_val_test_split(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = remove_labels(train_set, 'calss')\n",
    "X_val, y_val = remove_labels(val_set, 'calss')\n",
    "X_test, y_test = remove_labels(test_set, 'calss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Escalando el conjunto de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es importante comprender que los árboles de decisión son algoritmos que **no requieren demasiada preparación de los datos** concretamente, no requieren la realización de escalado o normalización. En este ejercicio se va a realizar escalado al conjunto de datos y se van a comparar los resultados con el conjunto de datos sin escalar. De esta forma se demuestra como aplicar preprocesamientos como el escalado puede incluso llegar a afectar al rendimiento del modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = RobustScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val_scaled = scaler.transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000000</td>\n",
       "      <td>0.764228</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.927632</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.286342</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         duration  total_fpackets  total_bpackets  total_fpktl  total_bpktl  \\\n",
       "508881  -0.013646             0.0             1.0    -0.310056     1.556886   \n",
       "208326  -0.013926             0.0             0.0     0.664804     0.000000   \n",
       "107213  -0.013926             0.0             0.0     0.703911     0.000000   \n",
       "466726  -0.000273             0.0             1.0    -0.363128     2.724551   \n",
       "230085  -0.013926             0.0             0.0     0.664804     0.000000   \n",
       "472961  34.421927             1.5             4.0     1.558659     3.868263   \n",
       "482372  -0.013805             0.5             0.0    -0.136872     0.000000   \n",
       "619993  17.578734             0.5             0.0    -0.050279     0.000000   \n",
       "65344   -0.013926             0.0             0.0     0.703911     0.000000   \n",
       "46666   -0.013926             0.0             0.0     0.505587     0.000000   \n",
       "\n",
       "        min_fpktl  min_bpktl  max_fpktl  max_bpktl  mean_fpktl  ...  \\\n",
       "508881   0.375000   4.924528  -0.027100   2.250000   -0.032895  ...   \n",
       "208326   6.607143   0.000000   0.918699   0.000000    1.115132  ...   \n",
       "107213   6.857143   0.000000   0.956640   0.000000    1.161184  ...   \n",
       "466726   0.035714   8.603774  -0.078591   3.931034   -0.095395  ...   \n",
       "230085   6.607143   0.000000   0.918699   0.000000    1.115132  ...   \n",
       "472961   0.000000   1.000000   1.341463   4.163793    0.337171  ...   \n",
       "482372   0.000000   0.000000   0.000000   0.000000   -0.050987  ...   \n",
       "619993   0.553571   0.000000   0.000000   0.000000    0.000000  ...   \n",
       "65344    6.857143   0.000000   0.956640   0.000000    1.161184  ...   \n",
       "46666    5.589286   0.000000   0.764228   0.000000    0.927632  ...   \n",
       "\n",
       "         min_idle  mean_idle   max_idle  std_idle  FFNEPD  \\\n",
       "508881   0.000000   0.000000   0.000000       0.0     0.0   \n",
       "208326   0.000000   0.000000   0.000000       0.0     0.0   \n",
       "107213   0.000000   0.000000   0.000000       0.0     0.0   \n",
       "466726   0.000000   0.000000   0.000000       0.0     0.0   \n",
       "230085   0.000000   0.000000   0.000000       0.0     0.0   \n",
       "472961  59.657102  48.116772  47.580946       0.0     0.0   \n",
       "482372   0.000000   0.000000   0.000000       0.0     1.0   \n",
       "619993  30.549478  24.617883  24.365465       0.0     1.0   \n",
       "65344    0.000000   0.000000   0.000000       0.0     0.0   \n",
       "46666    0.000000   0.000000   0.000000       0.0     0.0   \n",
       "\n",
       "        Init_Win_bytes_forward  Init_Win_bytes_backward  RRT_samples_clnt  \\\n",
       "508881               -0.286342                 0.000011               0.0   \n",
       "208326               -0.286342                 0.000000               0.0   \n",
       "107213               -0.286342                 0.000000               0.0   \n",
       "466726               -0.286342                 0.000011               0.0   \n",
       "230085               -0.286342                 0.000000               0.0   \n",
       "472961               13.421042                12.657580               3.0   \n",
       "482372               -0.080736                 0.000000               0.0   \n",
       "619993                0.063376                 0.000000               0.0   \n",
       "65344                -0.286342                 0.000000               0.0   \n",
       "46666                -0.286342                 0.000000               0.0   \n",
       "\n",
       "        Act_data_pkt_forward  min_seg_size_forward  \n",
       "508881                   0.0                  -1.0  \n",
       "208326                   0.0                  -1.0  \n",
       "107213                   0.0                  -1.0  \n",
       "466726                   0.0                  -1.0  \n",
       "230085                   0.0                  -1.0  \n",
       "472961                   1.5                   0.0  \n",
       "482372                   0.5                   0.0  \n",
       "619993                   0.5                   0.0  \n",
       "65344                    0.0                  -1.0  \n",
       "46666                    0.0                  -1.0  \n",
       "\n",
       "[10 rows x 79 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transformación a un DataFrame de Pandas\n",
    "X_train_scaled = DataFrame(X_train_scaled, columns=X_train.columns, index=X_train.index)\n",
    "X_train_scaled.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>total_fpackets</th>\n",
       "      <th>total_bpackets</th>\n",
       "      <th>total_fpktl</th>\n",
       "      <th>total_bpktl</th>\n",
       "      <th>min_fpktl</th>\n",
       "      <th>min_bpktl</th>\n",
       "      <th>max_fpktl</th>\n",
       "      <th>max_bpktl</th>\n",
       "      <th>mean_fpktl</th>\n",
       "      <th>...</th>\n",
       "      <th>min_idle</th>\n",
       "      <th>mean_idle</th>\n",
       "      <th>max_idle</th>\n",
       "      <th>std_idle</th>\n",
       "      <th>FFNEPD</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <th>RRT_samples_clnt</th>\n",
       "      <th>Act_data_pkt_forward</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>3.791730e+05</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "      <td>379173.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.543409</td>\n",
       "      <td>2.927643</td>\n",
       "      <td>10.762678</td>\n",
       "      <td>1.947184</td>\n",
       "      <td>75.104138</td>\n",
       "      <td>1.600682</td>\n",
       "      <td>0.855241</td>\n",
       "      <td>0.491724</td>\n",
       "      <td>1.591823</td>\n",
       "      <td>0.303639</td>\n",
       "      <td>...</td>\n",
       "      <td>19.838596</td>\n",
       "      <td>16.279309</td>\n",
       "      <td>16.441899</td>\n",
       "      <td>4.686618e+05</td>\n",
       "      <td>0.362658</td>\n",
       "      <td>2.861506</td>\n",
       "      <td>3.435883</td>\n",
       "      <td>10.087709</td>\n",
       "      <td>2.924548</td>\n",
       "      <td>-0.376798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>116.165117</td>\n",
       "      <td>92.319402</td>\n",
       "      <td>370.875546</td>\n",
       "      <td>185.549801</td>\n",
       "      <td>3071.968461</td>\n",
       "      <td>2.817301</td>\n",
       "      <td>1.676098</td>\n",
       "      <td>0.786500</td>\n",
       "      <td>3.210186</td>\n",
       "      <td>0.533388</td>\n",
       "      <td>...</td>\n",
       "      <td>201.477623</td>\n",
       "      <td>162.562856</td>\n",
       "      <td>160.822696</td>\n",
       "      <td>6.188642e+06</td>\n",
       "      <td>3.836035</td>\n",
       "      <td>5.577212</td>\n",
       "      <td>7.354164</td>\n",
       "      <td>369.660712</td>\n",
       "      <td>92.301146</td>\n",
       "      <td>0.466297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.013936</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.513966</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.946429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.227642</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.273026</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.286345</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.013926</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.321229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.084011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.101974</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.286342</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.986074</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.678771</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.915989</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.898026</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.713658</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25277.128872</td>\n",
       "      <td>24127.000000</td>\n",
       "      <td>74768.000000</td>\n",
       "      <td>113118.041899</td>\n",
       "      <td>622288.473054</td>\n",
       "      <td>23.892857</td>\n",
       "      <td>26.245283</td>\n",
       "      <td>3.840108</td>\n",
       "      <td>11.991379</td>\n",
       "      <td>4.299342</td>\n",
       "      <td>...</td>\n",
       "      <td>43893.471528</td>\n",
       "      <td>35408.189283</td>\n",
       "      <td>35008.286082</td>\n",
       "      <td>5.670000e+08</td>\n",
       "      <td>2267.000000</td>\n",
       "      <td>13.421042</td>\n",
       "      <td>46.346741</td>\n",
       "      <td>74524.000000</td>\n",
       "      <td>24127.000000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            duration  total_fpackets  total_bpackets    total_fpktl  \\\n",
       "count  379173.000000   379173.000000   379173.000000  379173.000000   \n",
       "mean       12.543409        2.927643       10.762678       1.947184   \n",
       "std       116.165117       92.319402      370.875546     185.549801   \n",
       "min        -0.013936       -0.500000        0.000000      -0.513966   \n",
       "25%        -0.013926        0.000000        0.000000      -0.321229   \n",
       "50%         0.000000        0.000000        0.000000       0.000000   \n",
       "75%         0.986074        1.000000        1.000000       0.678771   \n",
       "max     25277.128872    24127.000000    74768.000000  113118.041899   \n",
       "\n",
       "         total_bpktl      min_fpktl      min_bpktl      max_fpktl  \\\n",
       "count  379173.000000  379173.000000  379173.000000  379173.000000   \n",
       "mean       75.104138       1.600682       0.855241       0.491724   \n",
       "std      3071.968461       2.817301       1.676098       0.786500   \n",
       "min         0.000000      -0.946429       0.000000      -0.227642   \n",
       "25%         0.000000       0.000000       0.000000      -0.084011   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         1.000000       1.000000       1.000000       0.915989   \n",
       "max    622288.473054      23.892857      26.245283       3.840108   \n",
       "\n",
       "           max_bpktl     mean_fpktl  ...       min_idle      mean_idle  \\\n",
       "count  379173.000000  379173.000000  ...  379173.000000  379173.000000   \n",
       "mean        1.591823       0.303639  ...      19.838596      16.279309   \n",
       "std         3.210186       0.533388  ...     201.477623     162.562856   \n",
       "min         0.000000      -0.273026  ...       0.000000       0.000000   \n",
       "25%         0.000000      -0.101974  ...       0.000000       0.000000   \n",
       "50%         0.000000       0.000000  ...       0.000000       0.000000   \n",
       "75%         1.000000       0.898026  ...       1.000000       1.000000   \n",
       "max        11.991379       4.299342  ...   43893.471528   35408.189283   \n",
       "\n",
       "            max_idle      std_idle         FFNEPD  Init_Win_bytes_forward  \\\n",
       "count  379173.000000  3.791730e+05  379173.000000           379173.000000   \n",
       "mean       16.441899  4.686618e+05       0.362658                2.861506   \n",
       "std       160.822696  6.188642e+06       3.836035                5.577212   \n",
       "min         0.000000  0.000000e+00       0.000000               -0.286345   \n",
       "25%         0.000000  0.000000e+00       0.000000               -0.286342   \n",
       "50%         0.000000  0.000000e+00       0.000000                0.000000   \n",
       "75%         1.000000  0.000000e+00       0.000000                0.713658   \n",
       "max     35008.286082  5.670000e+08    2267.000000               13.421042   \n",
       "\n",
       "       Init_Win_bytes_backward  RRT_samples_clnt  Act_data_pkt_forward  \\\n",
       "count            379173.000000     379173.000000         379173.000000   \n",
       "mean                  3.435883         10.087709              2.924548   \n",
       "std                   7.354164        369.660712             92.301146   \n",
       "min                   0.000000          0.000000             -0.500000   \n",
       "25%                   0.000000          0.000000              0.000000   \n",
       "50%                   0.000000          0.000000              0.000000   \n",
       "75%                   1.000000          1.000000              1.000000   \n",
       "max                  46.346741      74524.000000          24127.000000   \n",
       "\n",
       "       min_seg_size_forward  \n",
       "count         379173.000000  \n",
       "mean              -0.376798  \n",
       "std                0.466297  \n",
       "min               -1.000000  \n",
       "25%               -1.000000  \n",
       "50%                0.000000  \n",
       "75%                0.000000  \n",
       "max                0.375000  \n",
       "\n",
       "[8 rows x 79 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Árbol de decisión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --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>DecisionTreeClassifier(max_depth=20, 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\"><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;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=20, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=20, random_state=42)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "MAX_DEPTH = 20\n",
    "\n",
    "# Modelo entrenado con el conjunto de datos sin escalar\n",
    "clf_tree = DecisionTreeClassifier(max_depth=MAX_DEPTH, random_state=42)\n",
    "clf_tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>DecisionTreeClassifier(max_depth=20, 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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=20, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=20, random_state=42)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modelo entrenado con el conjunto de datos escalado\n",
    "clf_tree_scaled = DecisionTreeClassifier(max_depth=MAX_DEPTH, random_state=42)\n",
    "clf_tree_scaled.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comenzar prediciendo con el propio conjunto de datos con el que se ha entrenado el algoritmo (train set), suele ser interesante para comprobar si se esta produciendo overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predecimos con el conjunto de datos de entrenamiento\n",
    "y_train_pred = clf_tree.predict(X_train)\n",
    "y_train_prep_pred = clf_tree_scaled.predict(X_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score WITHOUT preparation: 0.9593915432471419\n",
      "f1_score WITH preparation: 0.9593876451031004\n"
     ]
    }
   ],
   "source": [
    "# Comparamos resultados entre escalado y sin escalar\n",
    "evaluate_result(y_train_pred, y_train, y_train_prep_pred, y_train, f1_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predecimos con el conjunto de datos de validación\n",
    "y_pred = clf_tree.predict(X_val)\n",
    "y_prep_pred = clf_tree_scaled.predict(X_val_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score WITHOUT preparation: 0.9335628011437564\n",
      "f1_score WITH preparation: 0.9335467321600616\n"
     ]
    }
   ],
   "source": [
    "# Comparamos resultados entre escalado y sin escalar\n",
    "evaluate_result(y_pred, y_val, y_prep_pred, y_val, f1_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aunque los resultados son muy similares, el modelo sin escalar es más rápido y proporciona resultados ligeramente superiores. **A partir de este punto, y, en función de los resultados que hemos visto, nos quedamos con la opción de no escalar nuestros conjuntos de datos**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Visualizando el limite de decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reducimos el número de atributos del conjunto de datos para visualizarlo mejor\n",
    "X_train_reduced = X_train[['min_flowpktl', 'flow_fin']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>DecisionTreeClassifier(max_depth=2, 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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generamos un modelo con el conjunto de datos reducido\n",
    "clf_tree_reduced = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
    "clf_tree_reduced.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representamos gráficamente el límite de decisión construido\n",
    "from matplotlib.colors import ListedColormap\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, plot_training=True, resolution=1000):\n",
    "    mins = X.min(axis=0) - 1\n",
    "    maxs = X.max(axis=0) + 1\n",
    "    x1, x2 = np.meshgrid(np.linspace(mins[0], maxs[0], resolution),\n",
    "                         np.linspace(mins[1], maxs[1], resolution))\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "    custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
    "    plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
    "    if plot_training:\n",
    "        plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"normal\")\n",
    "        plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"adware\")\n",
    "        plt.plot(X[:, 0][y==2], X[:, 1][y==2], \"g^\", label=\"malware\")\n",
    "        plt.axis([mins[0], maxs[0], mins[1], maxs[1]])               \n",
    "    plt.xlabel('min_flowpktl', fontsize=14)\n",
    "    plt.ylabel('flow_fin', fontsize=14, rotation=90)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plot_decision_boundary(clf_tree_reduced, X_train_reduced.values, y_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<graphviz.sources.Source at 0x2170768b410>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pintamos el árbol para compararlo con la representación gráfica anterior\n",
    "from graphviz import Source\n",
    "from sklearn.tree import export_graphviz\n",
    "import os\n",
    "\n",
    "export_graphviz(\n",
    "        clf_tree_reduced,\n",
    "        out_file=\"android_malware.dot\",\n",
    "        feature_names=X_train_reduced.columns,\n",
    "        class_names=[\"benign\", \"adware\", \"malware\"],\n",
    "        rounded=True,\n",
    "        filled=True\n",
    "    )\n",
    "\n",
    "Source.from_file(\"android_malware.dot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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