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

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

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.