Visualizing regression model performance

Now you have seen how to evaluate multiple models out of the box, you will build three regression models to predict a song's "energy" levels.

The music_df dataset has had dummy variables for "genre" added. Also, feature and target arrays have been created, and these have been split into X_train, X_test, y_train, and y_test.

The following have been imported for you: LinearRegression, Ridge, Lasso, cross_val_score, and KFold.

Write a for loop using model as the iterator, and model.values() as the iterable.
Perform cross-validation on the training features and the training target array using the model, setting cv equal to the KFold object.
Append the model's cross-validation scores to the results list.
Create a box plot displaying the results, with the x-axis labels as the names of the models.

Exercise

Visualizing regression model performance

Instructions