# Import DecisionTreeClassifier from sklearn.tree
from sklearn.tree import DecisionTreeClassifier
# Instantiate a DecisionTreeClassifier 'dt' with a maximum depth of 6
dt = DecisionTreeClassifier(max_depth=6, criterion='entropy///gini', random_state=1)
# Fit dt to the training set
dt.fit(X_train, y_train)
# Predict test set labels
y_pred = dt.predict(X_test)
print(y_pred[0:5])
# Import accuracy_score
from sklearn.metrics import accuracy_score
# Compute test set accuracy
acc = accuracy_score(y_test, y_pred)
print("Test set accuracy: {:.2f}".format(acc))