I an using scikit-learn as a toolset.
I have 1K features as candidates and am trying to reduce the feature set as I believe the majority is noise (but am not sure).
I wanted to somehow automate this using PCA and Random Forests.
My end result would be a designated feature set.
Any suggestions? I know random forests provides feature_importances that can be matched to feature_names.
I know PCA can looks at features in terms of percentile that account for varience.
Before I dive in the shark tank, any advice would be greatly appreciated. Thanks, Chris