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.

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.

Exercise

Evaluate the regression tree

Instructions