As shown earlier, we can create a decision tree using scikit-learn's tree module. For now, let's not specify any optional arguments:
In : from sklearn import tree... dtc = tree.DecisionTreeClassifier()
Do you remember how to train the decision tree?
In : dtc.fit(X_train, y_train)Out: DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None, max_features=None, max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, presort=False, random_state=None, splitter='best')
Since we did not specify any pre-pruning parameters, we would expect this decision tree to grow quite large and result in a perfect score on the training set: ...