'''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.'''

# Import DecisionTreeClassifier from sklearn.tree
from sklearn.tree import DecisionTreeClassifier

# Instantiate dt_entropy, set 'entropy' as the information criterion
dt_entropy = DecisionTreeClassifier(max_depth=8, criterion='entropy', random_state=1)
dt_gini = DecisionTreeClassifier(max_depth=8, criterion='gini', random_state=1)


# Fit dt_entropy to the training set
dt_entropy.fit(X_train, y_train)
dt_gini.fit(X_train,y_train)