Split out the train and test sets

The first step of training a model is dividing the data into train and test sets. The tidymodels package makes this easy. Setting aside a test data set allows you to evaluate the trained model on a set of data the model has never seen.

You will use the employee healthcare attrition data which contains data about employees of a healthcare company and whether they left the company or not. It is available in attrition_df. The target variable is Attrition.

The tidyverse and tidymodels packages have been loaded for you.

Initialize a split of the data with 80% for training and stratify based on Attrition, the target variable.
Extract the training data set and store it in train.
Extract the testing data set and store it in test.

Exercise

Split out the train and test sets

Instructions