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Exercise

Evaluate the regression tree

In this exercise, you will evaluate the test set performance of dt using the Root Mean Squared Error (RMSE) metric. The RMSE of a model measures, on average, how much the model's predictions differ from the actual labels. The RMSE of a model can be obtained by computing the square root of the model's Mean Squared Error (MSE).

The features matrix X_test, the array y_test, as well as the decision tree regressor dt that you trained in the previous exercise are available in your workspace.

Instructions

100 XP
  • Import the function mean_squared_error as MSE from sklearn.metrics.
  • Predict the test set labels and assign the output to y_pred.
  • Compute the test set MSE by calling MSE and assign the result to mse_dt.
  • Compute the test set RMSE and assign it to rmse_dt.