Python数据挖掘—分类—随机森林

概念

随机森林(RandomForest)：随机森林是一个包含多个决策树的分类器，并且其输出的类别是由个别数输出的类别的众数而定

优点：适合离散型和连续型的属性数据；对海量数据，尽量避免了过度拟合的问题；对高维数据，不会出现特征选择困难的问题；实现简单，训练速度快，适合 进行分布式计算

 1 import pandas;
 2 
 3 data = pandas.read_csv(
 4     "D:\\PDM\\5.3\\data.csv"
 5 );
 6 
 7 dummyColumns = ["Gender", "ParentEncouragement"]
 8 
 9 for column in dummyColumns:
10     data[column]=data[column].astype('category')
11 
12 dummiesData = pandas.get_dummies(
13     data, 
14     columns=dummyColumns,
15     prefix=dummyColumns,
16     prefix_sep="=",
17     drop_first=True
18 )
19 dummiesData.columns
20 
21 fData = dummiesData[[
22     'ParentIncome', 'IQ', 'Gender=Male',
23     'ParentEncouragement=Not Encouraged'
24 ]]
25 
26 tData = dummiesData["CollegePlans"]
27 
28 from sklearn.tree import DecisionTreeClassifier
29 from sklearn.ensemble import RandomForestClassifier
30 from sklearn.model_selection import cross_val_score
31 
32 dtModel = DecisionTreeClassifier()
33 
34 dtScores = cross_val_score(
35     dtModel, 
36     fData, tData, cv=10
37 )
38 
39 dtScores.mean()
40 
41 rfcModel = RandomForestClassifier()
42 
43 rfcScores = cross_val_score(
44     rfcModel, 
45     fData, tData, cv=10
46 )
47 
48 rfcScores.mean()

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