I am doing multiple regression analysis, in which i want to eliminate some of the insignificant features. In most of the machine learning books subset selection, shrinkage methods or PCA is used for reducing number of feature. Why p-values are not commonly used for feature selection?

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    $\begingroup$ This is why. (Whether you do it in a stepwise manner or all at once doesn't change the fundamental problem.) $\endgroup$ Nov 19, 2015 at 8:20
  • $\begingroup$ @Stephan : I read the answer. Does it imply p-values should never be used? $\endgroup$
    – Siddhesh
    Nov 19, 2015 at 12:40
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    $\begingroup$ No. You can use and interpret p values if you use them correctly. This is a good place to start understanding them. In your specific case, if you look at multiple models (by selecting features), the p values will not be uniformly distributed under the null hypothesis any more, so you either need to find their new distribution (e.g., through simulation) or interpret them differently. $\endgroup$ Nov 19, 2015 at 12:45


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