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
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_testandpred_votetoclassification_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)