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.
Deze oefening maakt deel uit van de cursus
Marketing Analytics: Predicting Customer Churn in Python
Oefeninstructies
- 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.
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# Import RandomForestClassifier
# Instantiate the classifier
clf = ____
# Fit to the training data
# Compute accuracy
print(____.____(____, ____))