1. import datasets

from sklearn.datasets import load_iris

iris = load_iris()
print iris.feature_names
print iris.target_names
print iris.data[0]
print iris.target[0]

2.tree

import numpy as np
from sklearn.datasets import load_iris
from sklearn import tree

iris = load_iris()
test_idx = [0, 50, 100]

# training data
train_target = np.delete(iris.target, test_idx)
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)

# viz code
from sklearn.externals.six import StringIO
import pydotplus
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 = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("iris.pdf")

print test_data[0], test_target[0]
print iris.feature_names, iris.target_names

3.graph

image.png

4.output

[ 5.1  3.5  1.4  0.2] 0
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] ['setosa' 'versicolor' 'virginica']