How important is p-value in Machine Learning? Scikit-Learn doesn't exhibit the p-values for your models. I'm used to look at the p-values - besides a few other factors - when choosing the variables to consider on my final model. However, p-values doesn't see a big deal for Scikit-Learn. Why is that? Isn't p-value important? Can I use a variable even though the p-value is considerably large?
In the question https://stackoverflow.com/questions/59908991/what-is-the-level-of-significance-considered-in-the-logistic-regression-using-sc/59911394#59911394 the user Matias says p-values are not used in Machine Learning. Is that true?
 A: $p$-values are used for hypothesis testing. In machine learning you don't have any hypothesis to test, & you don't care about it. You care about making accurate predictions and hypothesis testing has nothing to do with it.
You can check the The Two Cultures: statistics vs. machine learning? thread for some related discussion.
A: Some Machine Learning techniques are based on p values, e.g., ANOVA feature selection.
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_classif.html
f_classif (ANOVA) returns a p-value for each feature. As Tim writes the p-value is used for hypothesis testing. ANOVA tests whether means of two or more samples are equal. A low p-value shows that at least 2 samples have different means which is a good indicator for a feature. Usually values below 0.1 or 0.05 or 0.01 mean that this feature could be used.
You could use for example SelectKBest (https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html) to take the first 10 features with the lowest p-values.
