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
DecisionTreeClassifier
fromsklearn.tree
. - Instantiate the classifier, storing the result in
clf
. - Train the classifier to the data. The features are contained in the
features
variable, 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(____)