Checking the out-of-bag score

Let's now check the out-of-bag score for the model from the previous exercise.

So far you've used the F1 score to measure performance. However, in this exercise you should use the accuracy score so that you can easily compare it to the out-of-bag score.

The decision tree classifier from the previous exercise, clf_dt, is available in your workspace.

The pokemon dataset is already loaded for you and split into train and test sets. In addition, the decision tree classifier was fit and is available for you as clf_dt to use it as base estimator.

Cet exercice fait partie du cours

Ensemble Methods in Python

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Instructions

Build the bagging classifier using the decision tree as base estimator and 21 estimators. This time, use the out-of-bag score by specifying an argument for the oob_score parameter.
Print the classifier's out-of-bag score.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Build and train the bagging classifier
clf_bag = ____(
  ____,
  ____,
  ____,
  random_state=500)
clf_bag.fit(X_train, y_train)

# Print the out-of-bag score
print('OOB-Score: {:.3f}'.format(____))

# Evaluate the performance on the test set to compare
pred = clf_bag.predict(X_test)
print('Accuracy: {:.3f}'.format(accuracy_score(y_test, pred)))

Modifier et exécuter le code