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.
Questo esercizio fa parte del corso
Marketing Analytics: Predicting Customer Churn in Python
Istruzioni dell'esercizio
- Predict the labels of the test set.
- Print the F1 score.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# 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(____)