机器学习之路: python 决策树分类 预测泰坦尼克号乘客是否幸存

 1 import pandas as pd
 2 from sklearn.cross_validation import train_test_split
 3 from sklearn.feature_extraction import DictVectorizer
 4 from sklearn.tree import DecisionTreeClassifier
 5 from sklearn.metrics import classification_report
 6 
 7 ‘‘‘
 8 决策树
 9 涉及多个特征，没有明显的线性关系
10 推断逻辑非常直观
11 不需要对数据进行标准化
12 ‘‘‘
13 
14 ‘‘‘
15 1 准备数据
16 ‘‘‘
17 # 读取泰坦尼克乘客数据，已经从互联网下载到本地
18 titanic = pd.read_csv("./data/titanic/titanic.txt")
19 # 观察数据发现有缺失现象
20 # print(titanic.head())
21 
22 # 提取关键特征，sex, age, pclass都很有可能影响是否幸免
23 x = titanic[[‘pclass‘, ‘age‘, ‘sex‘]]
24 y = titanic[‘survived‘]
25 # 查看当前选择的特征
26 # print(x.info())
27 ‘‘‘
28 <class ‘pandas.core.frame.DataFrame‘>
29 RangeIndex: 1313 entries, 0 to 1312
30 Data columns (total 3 columns):
31 pclass    1313 non-null object
32 age       633 non-null float64
33 sex       1313 non-null object
34 dtypes: float64(1), object(2)
35 memory usage: 30.9+ KB
36 None
37 ‘‘‘
38 # age数据列 只有633个，对于空缺的 采用平均数或者中位数进行补充 希望对模型影响小
39 x[‘age‘].fillna(x[‘age‘].mean(), inplace=True)
40 
41 ‘‘‘
42 2 数据分割
43 ‘‘‘
44 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=33)
45 # 使用特征转换器进行特征抽取
46 vec = DictVectorizer()
47 # 类别型的数据会抽离出来 数据型的会保持不变
48 x_train = vec.fit_transform(x_train.to_dict(orient="record"))
49 # print(vec.feature_names_)   # [‘age‘, ‘pclass=1st‘, ‘pclass=2nd‘, ‘pclass=3rd‘, ‘sex=female‘, ‘sex=male‘]
50 x_test = vec.transform(x_test.to_dict(orient="record"))
51 
52 ‘‘‘
53 3 训练模型 进行预测
54 ‘‘‘
55 # 初始化决策树分类器
56 dtc = DecisionTreeClassifier()
57 # 训练
58 dtc.fit(x_train, y_train)
59 # 预测 保存结果
60 y_predict = dtc.predict(x_test)
61 
62 ‘‘‘
63 4 模型评估
64 ‘‘‘
65 print("准确度:", dtc.score(x_test, y_test))
66 print("其他指标：\n", classification_report(y_predict, y_test, target_names=[‘died‘, ‘survived‘]))
67 ‘‘‘
68 准确度: 0.7811550151975684
69 其他指标：
70               precision    recall  f1-score   support
71 
72        died       0.91      0.78      0.84       236
73    survived       0.58      0.80      0.67        93
74 
75 avg / total       0.81      0.78      0.79       329
76 ‘‘‘