Exercise

Using entropy as a criterion

In this exercise, you'll train a classification tree on the Wisconsin Breast Cancer dataset using entropy as an information criterion. You'll do so using all the 30 features in the dataset, which is split into 80% train and 20% test.

X_train as well as the array of labels y_train are available in your workspace.

Instructions

100 XP

Import DecisionTreeClassifier from sklearn.tree.
Instantiate a DecisionTreeClassifier dt_entropy with a maximum depth of 8.
Set the information criterion to 'entropy'.
Fit dt_entropy on the training set.