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Tree-based AdaBoost regression

AdaBoost models are usually built with decision trees as the base estimators. Let's give this a try now and see if model performance improves even further.

We'll use twelve estimators as before to have a fair comparison. There's no need to instantiate the decision tree as it is the base estimator by default.

Este exercício faz parte do curso

Ensemble Methods in Python

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Instruções do exercício

  • Build and fit an AdaBoostRegressor using 12 estimators. You do not have to specify a base estimator.
  • Calculate the predictions on the test set.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Build and fit a tree-based AdaBoost regressor
reg_ada = ____(____, random_state=500)
reg_ada.fit(X_train, y_train)

# Calculate the predictions on the test set
pred = ____

# Evaluate the performance using the RMSE
rmse = np.sqrt(mean_squared_error(y_test, pred))
print('RMSE: {:.3f}'.format(rmse))
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