decision_tree_gini

from sklearn import tree

X = [[0, 0, 0], [0, 1, 1], [1, 0, 0], [1, 1, 0], [1, 1, 1]]
Y = [0, 1, 1, 0, 1]

feature_names = ['x1','x2','x3']
target_names = ['0','1']

clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)

# exportar em formato gráfico
import graphviz
dot_data = tree.export_graphviz(clf, out_file=None, filled=False, rounded=True, impurity=True,
                                class_names=target_names, feature_names=feature_names )

graph = graphviz.Source(dot_data)
graph.render("graph")

# exportar em formato texto
r = tree.export_text(clf,feature_names=feature_names)
print('\n'+r)