理论

特征筛选的作用

样本中的有些特征是所谓的“优秀特征”，使用这些特征可以显著的提高泛化能力。而有些特征在样本类别区分上并不明显，在训练中引入这些特征会导致算力的浪费；另外有些特征对样本的分类有反作用，引入这些特征反而会导致泛化能力下降

特征筛选

与PCA（主成分分析）不同，特征筛选不修改特征值，而是寻找对模型性能提升较大的尽量少的特征

代码实现

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

引入数据集——Titanic数据集

1 2	titan = pd.read_csv("http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt") titan.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1313 entries, 0 to 1312
Data columns (total 11 columns):
row.names    1313 non-null int64
pclass       1313 non-null object
survived     1313 non-null int64
name         1313 non-null object
age          633 non-null float64
embarked     821 non-null object
home.dest    754 non-null object
room         77 non-null object
ticket       69 non-null object
boat         347 non-null object
sex          1313 non-null object
dtypes: float64(1), int64(2), object(8)
memory usage: 112.9+ KB

数据预处理

分离数据与标签

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x_source = titan.drop(["row.names","name","survived"],axis=1)
x_source.info()
y_source = titan["survived"]

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1313 entries, 0 to 1312
Data columns (total 8 columns):
pclass       1313 non-null object
age          633 non-null float64
embarked     821 non-null object
home.dest    754 non-null object
room         77 non-null object
ticket       69 non-null object
boat         347 non-null object
sex          1313 non-null object
dtypes: float64(1), object(7)
memory usage: 82.1+ KB

缺失数据填充

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x_source['age'].fillna(x_source['age'].mean(),inplace=True)
x_source.fillna('UNKNOWN',inplace=True)
x_source.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1313 entries, 0 to 1312
Data columns (total 8 columns):
pclass       1313 non-null object
age          1313 non-null float64
embarked     1313 non-null object
home.dest    1313 non-null object
room         1313 non-null object
ticket       1313 non-null object
boat         1313 non-null object
sex          1313 non-null object
dtypes: float64(1), object(7)
memory usage: 82.1+ KB

数据分割

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from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x_source,y_source,random_state=33,test_size=0.25)
x_train.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 984 entries, 1086 to 1044
Data columns (total 8 columns):
pclass       984 non-null object
age          984 non-null float64
embarked     984 non-null object
home.dest    984 non-null object
room         984 non-null object
ticket       984 non-null object
boat         984 non-null object
sex          984 non-null object
dtypes: float64(1), object(7)
memory usage: 69.2+ KB

特征向量化

from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer()
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))

1	len(vec.feature_names_)

模型训练

1	from sklearn.tree import DecisionTreeClassifier

基本决策树模型

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dt = DecisionTreeClassifier(criterion='entropy')
dt.fit(x_train,y_train)
dt.score(x_test,y_test)

0.82066869300911849

带特征筛选的决策树

from sklearn import feature_selection
fs = feature_selection.SelectPercentile(feature_selection.chi2,percentile=7)
x_train_fs = fs.fit_transform(x_train,y_train)
x_test_fs = fs.transform(x_test)
print(x_train.shape,x_train_fs.shape)

(984, 474) (984, 33)

1 2	dt.fit(x_train_fs,y_train) dt.score(x_test_fs,y_test)

0.85410334346504557