Restricted and unrestricted decision trees
For this exercise, we will revisit the Pokémon dataset from the last chapter. Recall that the goal is to predict whether or not a given Pokémon is legendary.
Here, you will build two separate decision tree classifiers. In the first, you will specify the parameters min_samples_leaf and min_samples_split, but not a maximum depth, so that the tree can fully develop without any restrictions.
In the second, you will specify some constraints by limiting the depth of the decision tree. By then comparing the two models, you'll better understand the notion of a "weak" learner.
This exercise is part of the course
Ensemble Methods in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Build unrestricted decision tree
clf = ____
clf.fit(X_train, y_train)
# Predict the labels
pred = clf.predict(X_test)
# Print the confusion matrix
cm = confusion_matrix(y_test, pred)
print('Confusion matrix:\n', cm)
# Print the F1 score
score = f1_score(y_test, pred)
print('F1-Score: {:.3f}'.format(score))