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  3. Ensemble Methods in Python

Exercise

Evaluating your ensemble

In the previous exercise, you built your first voting classifier. Let's now evaluate it and compare it to that of the individual models.

The individual models (clf_knn, clf_dt, and clf_lr) and the voting classifier (clf_vote) have already been loaded and trained.

Remember to use f1_score() to evaluate the performance. In addition, you'll create a classification report on the test set (X_test, y_test) using the classification_report() function.

Will your voting classifier beat the 58% F1-score of the decision tree?

Instructions

100 XP
  • Use the voting classifier, clf_vote, to predict the labels of the test set, X_test.
  • Calculate the F1-Score of the voting classifier.
  • Calculate the classification report of the voting classifier by passing in y_test and pred_vote to classification_report().