Decision Trees

http://scikit-learn.org/stable/modules/tree.html



In [1]:

    
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
iris = load_iris()
X = iris.data[:, 2:] # petal length and width
y = iris.target
tree_clf = DecisionTreeClassifier(max_depth=2)
tree_clf.fit(X, y)









    Out[1]:





DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')



In [4]:

    
from sklearn.tree import export_graphviz

dot_data = export_graphviz(
 tree_clf, out_file=None,
 feature_names=iris.feature_names[2:],
 class_names=iris.target_names,
 rounded=True,
 filled=True
 )

graph = graphviz.Source(dot_data)
graph









    Out[4]:



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