Python sklearn - 如何计算p值

Question

这可能是一个简单的问题,但我试图使用分类器的分类器或回归的回归量来计算我的特征的p值.有人可以建议每个案例的最佳方法是什么,并提供示例代码？我想只看到每个功能的p值,而不是像文档中所解释的那样保持功能等的k最佳/百分位数.

谢谢

Answer 1

X, y直接运行重要性测试.使用20news的示例和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])

Answer 2

您可以使用statsmodels

import statsmodels.api as sm
logit_model=sm.Logit(y_train,X_train)
result=logit_model.fit()
print(result.summary())

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