随机森林实战

 1 # -*- coding: utf-8 -*-
 2 """
 3 Created on Tue Sep  4 09:38:57 2018
 4 
 5 @author: zhen
 6 """
 7 
 8 from sklearn.ensemble import RandomForestClassifier
 9 from sklearn.model_selection import train_test_split
10 from sklearn.metrics import accuracy_score
11 from sklearn.datasets import load_iris
12 import matplotlib.pyplot as plt
13 
14 iris = load_iris()
15 x = iris.data[:, :2]
16 y = iris.target
17 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
18 
19 # n_estimators:森林中树的个数（默认为10），建议为奇数
20 # n_jobs:并行执行任务的个数（包括模型训练和预测），默认值为-1，表示根据核数
21 rnd_clf = RandomForestClassifier(n_estimators=15, max_leaf_nodes=16, n_jobs=1)
22 rnd_clf.fit(x_train, y_train)
23 
24 y_predict_rf = rnd_clf.predict(x_test)
25 
26 print(accuracy_score(y_test, y_predict_rf))
27 
28 for name, score in zip(iris[‘feature_names‘], rnd_clf.feature_importances_):
29     print(name, score)
30     
31 # 可视化
32 plt.plot(x_test[:, 0], y_test, ‘r.‘, label=‘real‘)
33 plt.plot(x_test[:, 0], y_predict_rf, ‘b.‘, label=‘predict‘)
34 plt.xlabel(‘sepal-length‘, fontsize=15)
35 plt.ylabel(‘type‘, fontsize=15)
36 plt.legend(loc="upper left")
37 plt.show()
38 
39 plt.plot(x_test[:, 1], y_test, ‘r.‘, label=‘real‘)
40 plt.plot(x_test[:, 1], y_predict_rf, ‘b.‘, label=‘predict‘)
41 plt.xlabel(‘sepal-width‘, fontsize=15)
42 plt.ylabel(‘type‘, fontsize=15)
43 plt.legend(loc="upper right")
44 plt.show()