In [5]:

    

# (p. 167ff) Decision Trees
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)

from sklearn.tree import export_graphviz

export_graphviz(
    tree_clf,
    out_file="iris_tree.dot",
    feature_names=iris.feature_names[2:],
    class_names=iris.target_names,
    rounded=True,
    filled=True
)


    

In [6]:

    

tree_clf.predict_proba([[5, 1.5]])


    

    
Out[6]:





array([[ 0.        ,  0.90740741,  0.09259259]])

In [7]:

    

tree_clf.predict([[5, 1.5]])


    

    
Out[7]:





array([1])

In [ ]: