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Please suggest a feature selection technique which selects features such that they explain target well but are not correlated with given know confounds.

This paper(section 2.3) suggests using a GAN for the same. I would prefer something other than a deep learning technique.

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Not sure if you are looking for hyper parameter tuning, can you describe your dataset and the number of features it has.

An alternate approach would be to use feature extraction from sklearn to build sparse matrices rather than keeping your categorical variables as they are, it would give you can idea which features are important.

A tutorial can be found here :

https://jakevdp.github.io/PythonDataScienceHandbook/05.04-feature-engineering.html

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  • $\begingroup$ this doesn't answer my question $\endgroup$ – claudius Mar 22 '18 at 4:21

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