Machine Learning with Swift

Training the decision tree classifier

Let's learn how to train the decision tree classifier as shown in the following code snippet:

In []: 
from sklearn import tree 
tree_model = tree.DecisionTreeClassifier(criterion='entropy', random_state=42) 
tree_model = tree_model.fit(X_train, y_train) 
tree_model 
Out[]: 
DecisionTreeClassifier(class_weight=None,  
            criterion='entropy', 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=42, splitter='best') 

The most interesting for us are the class attributes of DecisionTreeClassifier:

• criterion: The way to estimate the best partition (see the How decision tree learning works section). ...