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).max_depth
: Maximum tree depth.max_features
: The maximum number of attributes to account in one split.min_samples_leaf
: The minimum number of objects in the leaf; for example, if it...