split_using_srs: Creating training and testing data based on stratified random...

In snfagora/autotextclassifier: R Package for Automatically Classifying Texts Based on Tidymodels

Creating training and testing data based on stratified random sampling (SRS) and preprocessing steps

Description

Creating training and testing data based on stratified random sampling (SRS) and preprocessing steps

Usage

split_using_srs(input_data, category, rec, prop_ratio = 0.8, pull_id = NULL)

Arguments

input_data	The data to be trained and tested.
category	The target binary category.
rec	The recipe (preprocessing steps) that will be applied to the training and test data
prop_ratio	The ratio used to split the data. The default value is 0.8
pull_id	The identifier used to identify training and test data values. The default value is NULL.

Value

A list output that contains train_x_class, test_x_class, train_y_class, test_y_class.

snfagora/autotextclassifier documentation built on June 16, 2022, 9:42 a.m.