Computing accuracy
Having split your data into training and testing sets, you can now fit your model to the training data and then predict the labels of the test data. That's what you'll practice doing in this exercise.
So far, you've used Logistic Regression and Decision Trees. Here, you'll use a RandomForestClassifier, which you can think of as an ensemble of Decision Trees that generally outperforms a single Decision Tree.
Your work in the previous exercises has carried over, and the training and test sets are available in the variables X_train, X_test, y_train, and y_test.
This exercise is part of the course
Marketing Analytics: Predicting Customer Churn in Python
Exercise instructions
- Import
RandomForestClassifierfromsklearn.ensemble. - Instantiate a
RandomForestClassifierasclf. - Fit
clfto the training data:X_trainandy_train. - Compute the accuracy of
clfon the testing data using the.score()method.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import RandomForestClassifier
# Instantiate the classifier
clf = ____
# Fit to the training data
# Compute accuracy
print(____.____(____, ____))