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Computing other metrics

In addition to accuracy, let's also compute the F1 score of this new model to get a better picture of model performance.

A 70-30 train-test split has already been done for you, and all necessary modules have been imported.

This exercise is part of the course

Marketing Analytics: Predicting Customer Churn in Python

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Exercise instructions

  • Predict the labels of the test set.
  • Print the F1 score.

Hands-on interactive exercise

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

# Import f1_score
from sklearn.metrics import f1_score

# Instantiate the classifier
clf = RandomForestClassifier()

# Fit to the data
clf.fit(X_train, y_train)

# Predict the labels of the test set
y_pred = ____

# Print the F1 score
print(____)
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