XGBClassifier是xgboost的sklearn版本。代码完整的展示了使用xgboost建立模型的过程，并比较xgboost和randomForest的性能。
1 # -*- coding: utf-8 -*-
2 """
3 # 作者：wanglei5205
4 # 邮箱：[email protected]
5 # 博客：http://cnblogs.com/wanglei5205
6 # github：http://github.com/wanglei5205
7 """
8 ### 导入模块
9 import pandas as pd
10
11 ### load_data
12 titanic = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')
13 X = titanic[['pclass', 'age', 'sex']] # 输入空间
14 y = titanic['survived'] # 输出空间
15 X.age.fillna(X.age.mean(), inplace=True) # 填充缺失值（均值），inplace=True(无返回值，原地替换）
16
17 ### split_data
18 from sklearn.cross_validation import train_test_split
19 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33)
20
21 ### feature_extraction
22 from sklearn.feature_extraction import DictVectorizer
23 vec = DictVectorizer(sparse=False)
24 X_train = vec.fit_transform(X_train.to_dict(orient='record'))
25 X_test = vec.transform(X_test.to_dict(orient='record'))
26
27 ### create_model
28 # rfc
29 from sklearn.ensemble import RandomForestClassifier
30 rfc = RandomForestClassifier()
31 rfc.fit(X_train, y_train)
32
33 # xgbc
34 from xgboost import XGBClassifier
35 xgbc = XGBClassifier()
36 xgbc.fit(X_train, y_train)
37
38 ### model_score
39 print ('rfc.score=',rfc.score(X_test, y_test))
40 print('xgbc.score=',xgbc.score(X_test, y_test))
41 """
42 rfc.score= 0.787234042553
43 xgbc.score= 0.787234042553
44 """