Python sklearn - 如何计算 p 值
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Python sklearn - how to calculate p-values
提问by user1096808
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.
这可能是一个简单的问题，但我试图计算我的特征的 p 值，要么使用分类器解决分类问题，要么使用回归器进行回归。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。
Thank you
谢谢
采纳答案by Fred Foo
Just run the significance test on X, ydirectly. Example using 20news and chi2:
X, y直接运行显着性检验。使用 20news 和的示例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])
回答by Lin Feng
You can use statsmodels
您可以使用统计模型
import statsmodels.api as sm
logit_model=sm.Logit(y_train,X_train)
result=logit_model.fit()
print(result.summary())
The resultswould be something like this
该结果会是这样的
Logit Regression Results
==============================================================================
Dep. Variable: y No. Observations: 406723
Model: Logit Df Residuals: 406710
Method: MLE Df Model: 12
Date: Fri, 12 Apr 2019 Pseudo R-squ.: 0.001661
Time: 16:48:45 Log-Likelihood: -2.8145e+05
converged: False LL-Null: -2.8192e+05
LLR p-value: 8.758e-193
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
x1 -0.0037 0.003 -1.078 0.281 -0.010 0.003
