# What value of ANOVA F-Value is significant

I'm using ANOVA F-Value from Scikit-learn (link) to find importance of features in a machine learning task. I have numeric feature and binary classification. I'm also filtering the features whose p-value is > 0.05, but I don't know what value for the ANOVA F-Value is good enough?

• Leaving aside whether what you're doing makes sense, which F corresponds to a given p-value is a function of the two degrees-of-freedom parameters. There's no single F value that goes with $p\approx 0.05$ Aug 26, 2017 at 2:02
• Could you explain why this approach is not making sense? If F-value is not suitable for feature importance, why it's included in the scikit-learn?
– HHH
Aug 28, 2017 at 13:54
• As for why it's there, that would best be asked by the people that decided to include it, but variable selection by backward elimination based on p-value is not a great way to choose variables, and p-value doesn't really measure importance. Some of the issues with such selection approaches I assume you're avoiding via cross validation or something similar, though. Aug 28, 2017 at 14:58
• I'm only filtering out features whose p-value is less that 0.05 because we don;t have sufficient confidence in the the F-Value for those features. After the filtering, I'm looking at the F-Value and select those that I think are more important (based on some buseiness logic). does this make sense?
– HHH
Aug 28, 2017 at 15:10

Don't do that. $p$-values of coefficients are a bad way to select models or features because they don't directly quantify any useful measures of model quality. Besides, while obtaining $p < α$ tells you that a null hypothesis is infeasible, obtaining $p > α$ doesn't much support its feasibility, so when a test fails to reject the null hypothesis, it doesn't tell you anything.