Evaluate the 10-fold CV error

In this exercise, you'll evaluate the 10-fold CV Root Mean Squared Error (RMSE) achieved by the regression tree dt that you instantiated in the previous exercise.

In addition to dt, the training data including X_train and y_train are available in your workspace. We also imported cross_val_score from sklearn.model_selection.

Note that since cross_val_score has only the option of evaluating the negative MSEs, its output should be multiplied by negative one to obtain the MSEs. The CV RMSE can then be obtained by computing the square root of the average MSE.

This exercise is part of the course

Machine Learning with Tree-Based Models in Python

Exercise instructions

Compute dt's 10-fold cross-validated MSE by setting the scoring argument to 'neg_mean_squared_error'.
Compute RMSE from the obtained MSE scores.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Compute the array containing the 10-folds CV MSEs
MSE_CV_scores = - ____(____, ____, ____, cv=____, 
                       ____='____',
                       n_jobs=-1)

# Compute the 10-folds CV RMSE
RMSE_CV = (____.____)**(____)

# Print RMSE_CV
print('CV RMSE: {:.2f}'.format(RMSE_CV))

Edit and Run Code

This exercise is part of the course

Machine Learning with Tree-Based Models in Python

IntermediateSkill Level

4.9+

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Current Exercise

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