train_data = np.delete(iris.data, test_idx, axis =0)
#testing data
test_target = iris.target[test_idx]
test_data = iris.data[test_idx]
clf = tree.DecisionTreeClassifier()
clf.fit(train_data, train_target)
print (test_target)
print (clf.predict(test_data))
#Explain later
from sklearn.externals.six import StringIO
import pydot
dot_data = StringIO()
tree.export_graphviz(clf,
out_file = dot_data,
feature_names = iris.feature_names,
class_names = iris.target_names,
filled = True, rounded = True,
impurity = False)
graph = pydot.graph_from_dot_data(dot_data.getValue())
graph.write_pdf("iris.pdf")
