Training another scikit-learn model
All sklearn models have .fit() and .predict() methods like the one you used in the previous exercise for the LogisticRegression model. This feature allows you to easily try many different models to see which one gives you the best performance. To get you more confident with using the sklearn API, in this exercise you'll try fitting a DecisionTreeClassifier instead of a LogisticRegression.
This exercise is part of the course
Marketing Analytics: Predicting Customer Churn in Python
Exercise instructions
- Import
DecisionTreeClassifierfromsklearn.tree. - Instantiate the classifier, storing the result in
clf. - Train the classifier to the data. The features are contained in the
featuresvariable, and the target variable of interest is'Churn'. - Predict the label of
new_customer.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import DecisionTreeClassifier
# Instantiate the classifier
# Fit the classifier
# Predict the label of new_customer
print(____)