在Scikit Learn中运行SelectKBest之后获取功能名称的最简单方法 [英] The easiest way for getting feature names after running SelectKBest in Scikit Learn

问题描述

我想进行监督学习.

直到现在，我还知道对所有功能进行有监督的学习.

Until now I know to do supervised learning to all features.

但是，我也想对K个最佳功能进行实验.

However, I would like also to conduct experiment with the K best features.

我阅读了文档，发现在Scikit中学习了SelectKBest方法.

I read the documentation and found the in Scikit learn there is SelectKBest method.

不幸的是，我不确定在找到这些最佳功能后如何创建新的数据框:

Unfortunately, I am not sure how to create new dataframe after finding those best features:

假设我想进行5种最佳功能的实验:

Let's assume I would like to conduct experiment with 5 best features:

from sklearn.feature_selection import SelectKBest, f_classif
select_k_best_classifier = SelectKBest(score_func=f_classif, k=5).fit_transform(features_dataframe, targeted_class)

现在，如果我要添加下一行:

Now if I would add the next line:

dataframe = pd.DataFrame(select_k_best_classifier)

我将收到一个不带功能名称的新数据帧(仅索引从0到4).

I will receive a new dataframe without feature names (only index starting from 0 to 4).

我应该将其替换为:

dataframe = pd.DataFrame(fit_transofrmed_features, columns=features_names)

我的问题是如何创建features_names列表?

My question is how to create the features_names list??

我知道我应该使用: select_k_best_classifier.get_support()

I know that I should use: select_k_best_classifier.get_support()

哪个返回布尔值数组.

数组中的true值代表右列中的索引.

The true value in the array represent the index in the right column.

如何将这个布尔数组与可以通过该方法获得的所有要素名称的数组一起使用:

How should I use this boolean array with the array of all features names I can get via the method:

feature_names = list(features_dataframe.columns.values)

推荐答案

您可以执行以下操作:

mask = select_k_best_classifier.get_support() #list of booleans
new_features = [] # The list of your K best features

for bool, feature in zip(mask, feature_names):
    if bool:
        new_features.append(feature)

然后更改功能名称:

dataframe = pd.DataFrame(fit_transofrmed_features, columns=new_features)

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