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

To view our screen cast, CLICK HERE.

To access our code, CLICK HERE.

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