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  3. Machine Learning with Tree-Based Models in Python

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.