Evaluate the GB regressor

Now that the test set predictions are available, you can use them to evaluate the test set Root Mean Squared Error (RMSE) of gb.

y_test and predictions y_pred are available in your workspace.

This exercise is part of the course

Machine Learning with Tree-Based Models in Python

Exercise instructions

Import mean_squared_error from sklearn.metrics as MSE.
Compute the test set MSE and assign it to mse_test.
Compute the test set RMSE and assign it to rmse_test.

Hands-on interactive exercise

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

# Import mean_squared_error as MSE
____

# Compute MSE
mse_test = ____

# Compute RMSE
rmse_test = ____

# Print RMSE
print('Test set RMSE of gb: {:.3f}'.format(rmse_test))

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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Exercise 1: Adaboost Exercise 2: Define the AdaBoost classifier Exercise 3: Train the AdaBoost classifier Exercise 4: Evaluate the AdaBoost classifier Exercise 5: Gradient Boosting (GB)Exercise 6: Define the GB regressor Exercise 7: Train the GB regressor Exercise 8: Evaluate the GB regressor

Current Exercise

Exercise 9: Stochastic Gradient Boosting (SGB)Exercise 10: Regression with SGB Exercise 11: Train the SGB regressor Exercise 12: Evaluate the SGB regressor

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