直接上代码，简单
1 # -*- coding: utf-8 -*-
2 """
3 ###############################################################################
4 # 作者：wanglei5205
5 # 邮箱：wanglei5205@126.com
6 # 代码：http://github.com/wanglei5205
7 # 博客：http://cnblogs.com/wanglei5205
8 # 目的：学习xgboost的plot_importance函数
9 # 官方API文档：http://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.training
10 ###############################################################################
11 """
12 ### load module
13 import matplotlib.pyplot as plt
14 from sklearn import datasets
15 from sklearn.model_selection import train_test_split
16 from sklearn.metrics import accuracy_score
17 from xgboost import XGBClassifier
18 from xgboost import plot_importance
19
20 ### load datasets
21 digits = datasets.load_digits()
22
23 ### data analysis
24 print(digits.data.shape)
25 print(digits.target.shape)
26
27 ### data split
28 x_train,x_test,y_train,y_test = train_test_split(digits.data,
29 digits.target,
30 test_size = 0.3,
31 random_state = 33)
32
33 model = XGBClassifier()
34 model.fit(x_train,y_train)
35
36 ### plot feature importance
37 fig,ax = plt.subplots(figsize=(15,15))
38 plot_importance(model,
39 height=0.5,
40 ax=ax,
41 max_num_features=64)
42 plt.show()
43
44 ### make prediction for test data
45 y_pred = model.predict(x_test)
46
47 ### model evaluate
48 accuracy = accuracy_score(y_test,y_pred)
49 print("accuarcy: %.2f%%" % (accuracy*100.0))
50 """
51 95.0%
52 """