In [1]:

    

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier

iris = load_iris()

X = iris.data[:,2:]
y = iris.target


    

In [2]:

    

tree_clf = DecisionTreeClassifier(max_depth=2)
tree_clf.fit(X,y)


    

    
Out[2]:





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

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 [5]:

    

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


    

    
Out[5]:





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

In [6]:

    

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


    

    
Out[6]:





array([1])

In [ ]: