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Exercise

Regularized regression: Ridge

Ridge regression performs regularization by computing the squared values of the model parameters multiplied by alpha and adding them to the loss function.

In this exercise, you will fit ridge regression models over a range of different alpha values, and print their \(R^2\) scores. You will use all of the features in the sales_df dataset to predict "sales". The data has been split into X_train, X_test, y_train, y_test for you.

A variable called alphas has been provided as a list containing different alpha values, which you will loop through to generate scores.

Instructions

100 XP
  • Import Ridge.
  • Instantiate Ridge, setting alpha equal to alpha.
  • Fit the model to the training data.
  • Calculate the \(R^2\) score for each iteration of ridge.