Learning Data Mining with Python - Second Edition

Applying random forests

Random forests in scikit-learn use the Estimator interface, allowing us to use almost the exact same code as before to do cross-fold validation:

from sklearn.ensemble import RandomForestClassifierclf = RandomForestClassifier(random_state=14)scores = cross_val_score(clf, X_teams, y_true, scoring='accuracy')print("Accuracy: {0:.1f}%".format(np.mean(scores) * 100))

This results in an immediate benefit of 65.3 percent, up by 2.5 points by just swapping the classifier.

Random forests, using subsets of the features, should be able to learn more effectively with more features than normal decision trees. We can test this by throwing more features at the algorithm and seeing how it goes:

 X_all = np.hstack([X_lastwinner, ...