Full linear fit works, but gives a lot of overfitting because there are too many predictors. Most of the predictors have little to no validity when used alone in simple t-test (last 2 pics).pic.twitter.com/3DRAmTASmY
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What's the state of the art #rstats #MachineLearning approach to penalized regression/variable selection with n = 1900, p = 1000 (all binary, many with low rate)? Goal = maximized validated predictive validity. Interpretability is nice but not necessarily.
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Thanks. Twitter will use this to make your timeline better. UndoUndo
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Already using it. Not really designed for binary predictors.
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XGBoost is what Kagglers usually start with before moving onto ensembling in caret.
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