I have a binary supervised classification problem with about 62 features, by eye about 30 of them could have reasonable discriminating power. I am using sklearn and the MLP does not have a dedicated feature selection tool like decision trees do. My question is what is the recommended way to preform feature selection here? I have read in the sklearn documentation that LDA should not be performed in a binary classification problem and PCA is under the unsupervised methods on the sklearn website.
Does anyone have any experience with this that could suggest a method?
(P.S. Apologies if this question isn't up to standard, this is my first question ever asked)