Python sklearn - 如何计算 p 值 [英] Python sklearn - how to calculate p-values

问题描述

这可能是一个简单的问题，但我正在尝试使用用于分类问题的分类器或用于回归的回归器来计算我的特征的 p 值.有人可以建议每种情况的最佳方法是什么并提供示例代码吗?我只想查看每个特征的 p 值，而不是像文档中解释的那样保留 k 个最佳/特征百分比等.

谢谢

解决方案

直接对 X, y 进行显着性检验即可.使用 20news 和 chi2 的示例:

<预><代码>>>>从 sklearn.datasets 导入 fetch_20newsgroups_vectorized>>>从 sklearn.feature_selection 导入 chi2>>>数据 = fetch_20newsgroups_vectorized()>>>X, y = 数据.数据, 数据.目标>>>分数，pvalues = chi2(X, y)>>>值数组([ 4.10171798e-17, 4.34003018e-01, 9.99999996e-01, ...,9.99999995e-01、9.99999869e-01、9.99981414e-01])

This is probably a simple question but I am trying to calculate the p-values for my features either using classifiers for a classification problem or regressors for regression. Could someone suggest what is the best method for each case and provide sample code? I want to just see the p-value for each feature rather than keep the k best / percentile of features etc as explained in the documentation.

Thank you

解决方案

Just run the significance test on X, y directly. Example using 20news and chi2:

>>> from sklearn.datasets import fetch_20newsgroups_vectorized
>>> from sklearn.feature_selection import chi2
>>> data = fetch_20newsgroups_vectorized()
>>> X, y = data.data, data.target
>>> scores, pvalues = chi2(X, y)
>>> pvalues
array([  4.10171798e-17,   4.34003018e-01,   9.99999996e-01, ...,
         9.99999995e-01,   9.99999869e-01,   9.99981414e-01])

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