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.

This exercise is part of the course

Machine Learning with Tree-Based Models in Python

Exercise instructions

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.

Hands-on interactive exercise

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

# Import mean_squared_error from sklearn.metrics as MSE
____

# Fit dt to the training set
____.____(____, ____)

# Predict the labels of the training set
____ = ____.____(____)

# Evaluate the training set RMSE of dt
____ = (____(____, ____))**(___)

# Print RMSE_train
print('Train RMSE: {:.2f}'.format(RMSE_train))

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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