TidyX 81: tidymodels logistic regression

This week, Ellis Hughes and I start exploring classification algorithms in {tidymodels}. We set up a logistic regression using NHL data to forecast whether a team will make the playoffs or not. Like all of our {tidymodels} episodes, we discuss:

1. Initializing the model
2. Splitting the data into training and test sets
3. Creating cross-validation folds of the training data
4. Setting up a model recipe
5. Creating a model workflow
6. Building and evaluating the model on the cross validation folds
7. Fitting the model to the test data
8. Evaluating the model predictions

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To access our code, CLICK HERE.

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