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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?

This exercise is part of the course

Ensemble Methods in Python

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Exercise instructions

  • 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().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Calculate the predictions using the voting classifier
pred_vote = ____

# Calculate the F1-Score of the voting classifier
score_vote = ____
print('F1-Score: {:.3f}'.format(score_vote))

# Calculate the classification report
report = ____
print(report)
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