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library(ggpage)
library(tidytext)
library(tidyverse)
text<-"Modeling as a statistical practice can encompass a wide variety of activities. This book focuses on supervised or predictive modeling for text, using text data to make predictions about the world around us. We use the tidymodels framework for modeling, a consistent and flexible collection of R packages developed to encourage good statistical practice.Supervised machine learning using text data involves building a statistical model to estimate some output from input that includes language. The two types of models we train in this book are regression and classification. Think of regression models as predicting numeric or continuous outputs, such as predicting the year of a United States Supreme Court opinion from the text of that opinion. Think of classification models as predicting outputs that are discrete quantities or class labels, such as predicting whether a GitHub issue is about documentation or not from the text of the issue. Models like these canbe used to make predictions for new observations, to understand what features or characteristics contribute to differences in the output, and more. We can evaluate our models using performance metrics to determine which are best, which are acceptable for our specific context, and even which are fair."
tibble(text=text) %>%
unnest_tokens(text, text, token=function(x) str_split(x, "\n")) %>%
ggpage_quick()
#> Warning: Use of `data_1$x_space_right` is discouraged. Use `x_space_right`#> instead.#> Warning: Use of `data_1$x_page` is discouraged. Use `x_page` instead.#> Warning: Use of `data_1$x_space_left` is discouraged. Use `x_space_left`#> instead.#> Warning: Use of `data_1$x_page` is discouraged. Use `x_page` instead.#> Warning: Use of `data_1$line` is discouraged. Use `line` instead.#> Warning: Use of `data_1$y_page` is discouraged. Use `y_page` instead.#> Warning: Use of `data_1$line` is discouraged. Use `line` instead.#> Warning: Use of `data_1$y_page` is discouraged. Use `y_page` instead.
text<-"Modeling as a statistical practice can encompass a wide variety of activities. This book focuses on supervised or predictive modeling for text, using text data to make predictions about the world around us. We use the tidymodels framework for modeling, a consistent and flexible collection of R packages developed to encourage good statistical practice.Supervised machine learning using text data involves building a statistical model to estimate some output from input that includes language. The two types of models we train in this book are regression and classification. Think of regression models as predicting numeric or continuous outputs, such as predicting the year of a United States Supreme Court opinion from the text of that opinion. Think of classification models as predicting outputs that are discrete quantities or class labels, such as predicting whether a GitHub issue is about documentation or not from the text of the issue. Models like these canbe used to make predictions for new observations, to understand what features or characteristics contribute to differences in the output, and more. We can evaluate our models using performance metrics to determine which are best, which are acceptable for our specific context, and even which are fair."
tibble(text=text) %>%
unnest_tokens(text, text, token=function(x) str_split(x, "\n")) %>%
ggpage_quick()
#> Warning: Use of `data_1$x_space_right` is discouraged. Use `x_space_right`#> instead.#> Warning: Use of `data_1$x_page` is discouraged. Use `x_page` instead.#> Warning: Use of `data_1$x_space_left` is discouraged. Use `x_space_left`#> instead.#> Warning: Use of `data_1$x_page` is discouraged. Use `x_page` instead.#> Warning: Use of `data_1$line` is discouraged. Use `line` instead.#> Warning: Use of `data_1$y_page` is discouraged. Use `y_page` instead.#> Warning: Use of `data_1$line` is discouraged. Use `line` instead.#> Warning: Use of `data_1$y_page` is discouraged. Use `y_page` instead.
Created on 2021-04-18 by the reprex package (v1.0.0)
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