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  3. Machine Learning with Tree-Based Models in Python

Exercise

Evaluate the training error

You'll now evaluate the training set RMSE achieved by the regression tree dt that you instantiated in a previous exercise.

In addition to dt, X_train and y_train are available in your workspace.

Note that in scikit-learn, the MSE of a model can be computed as follows:

MSE_model = mean_squared_error(y_true, y_predicted)

where we use the function mean_squared_error from the metrics module and pass it the true labels y_true as a first argument, and the predicted labels from the model y_predicted as a second argument.

Instructions

100 XP
  • Import mean_squared_error as MSE from sklearn.metrics.
  • Fit dt to the training set.
  • Predict dt's training set labels and assign the result to y_pred_train.
  • Evaluate dt's training set RMSE and assign it to RMSE_train.