有多少人工,就有多少智能

特征选择---SelectKBest

 

from sklearn.feature_selection import SelectKBest

http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest.set_params

 
class SelectKBest(_BaseFilter):
 
"""Select features according to the k highest scores.
 
 
 
Read more in the :ref:`User Guide <univariate_feature_selection>`.
 
 
 
Parameters
 
----------
 
score_func : callable
 
Function taking two arrays X and y, and returning a pair of arrays
 
(scores, pvalues) or a single array with scores.
 
Default is f_classif (see below "See also"). The default function only
 
works with classification tasks.
 
 
 
k : int or "all", optional, default=10
 
Number of top features to select.
 
The "all" option bypasses selection, for use in a parameter search.
 
 
 
Attributes
 
----------
 
scores_ : array-like, shape=(n_features,)
 
Scores of features.
 
 
 
pvalues_ : array-like, shape=(n_features,)
 
p-values of feature scores, None if `score_func` returned only scores.
 
 
 
Notes
 
-----
 
Ties between features with equal scores will be broken in an unspecified
 
way.
 
 
 
See also
 
--------
 
f_classif: ANOVA F-value between label/feature for classification tasks.
 
mutual_info_classif: Mutual information for a discrete target.
 
chi2: Chi-squared stats of non-negative features for classification tasks.
 
f_regression: F-value between label/feature for regression tasks.
 
mutual_info_regression: Mutual information for a continuous target.
 
SelectPercentile: Select features based on percentile of the highest scores.
 
SelectFpr: Select features based on a false positive rate test.
 
SelectFdr: Select features based on an estimated false discovery rate.
 
SelectFwe: Select features based on family-wise error rate.
 
GenericUnivariateSelect: Univariate feature selector with configurable mode.
 
"""

 

 官网的一个例子(需要自己给出计算公式、和k值)

参数

1、score_func : callable,函数取两个数组X和y,返回一对数组(scores, pvalues)或一个分数的数组。默认函数为f_classif,默认函数只适用于分类函数。
2、k:int or "all", optional, default=10。所选择的topK个特征。“all”选项则绕过选择,用于参数搜索。

属性

1、scores_ : array-like, shape=(n_features,),特征的得分
2、pvalues_ : array-like, shape=(n_features,),特征得分的p_value值,如果score_func只返回分数,则返回None。

score_func里可选的公式

方法

1、fit(X,y),在(X,y)上运行记分函数并得到适当的特征。
2、fit_transform(X[, y]),拟合数据,然后转换数据。
3、get_params([deep]),获得此估计器的参数。
4、get_support([indices]),获取所选特征的掩码或整数索引。
5、inverse_transform(X),反向变换操作。
6、set_params(**params),设置估计器的参数。
7、transform(X),将X还原为所选特征。



如何返回选择特征的名称或者索引。其实在上面的方法中已经提了一下了,那就是get_support()


之前的digit数据是不带特征名称的,我选择了带特征的波士顿房价数据,因为是回归数据,所以计算的评价指标也跟着变换了,f_regression,这里需要先fit一下,才能使用get_support()。里面的参数如果索引选择True,

返回值就是feature的索引,可能想直接返回feature name在这里不能这么直接的调用了,但是在dataset里面去对应一下应该很容易的。这里我给出的K是5,选择得分最高的前5个特征,分别是第2,5,9,10,12个属性。
如果里面的参数选择了False,返回值就是该特征是否被选择的Boolean值。



 

链接:https://www.jianshu.com/p/586ba8c96a3d

posted @ 2021-01-12 20:01  lvdongjie-avatarx  阅读(930)  评论(0编辑  收藏  举报