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My question is an extension to the question asked here. How does one identify the parity of predictor/feature/variable impact on response/outcome in a data mining model. Is there a standard procedure to find the 'direction' of impact after one does feature selection and derives variable importance using methods such as regularized random forest or lasso/elasticnet?

I know this question may sound quite naive, but I really wanted to know and I have searched SO and other materials but couldn't find a convincing answer.

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  • I don't thing there is a standard approach.
  • "Boosted Regression Trees for ecological modeling" is a commonly cited reference that briefly discusses some of these issues in the context of boosting. Partial dependence plots are available in many packages.
  • Rminer is a package the uses sensitivity analysis to extract information from models. Underused and has the benefit that you can use almost any model with this methodology.
  • Soren Welling is an active member on this site - has authored the [forestFloor][2] package and goes into some depth into getting information from blackbox models in the following stackexchange answer: Getting Information out of Blackbox Models - RandomForest / XGBoost
  • I haven't looked at the ggforest package, but this also offers to open the black box.
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  • $\begingroup$ Thx for the referral :) - pkg is called forestFloor $\endgroup$ Jan 3, 2016 at 15:06
  • $\begingroup$ aaaah. Sorry about that. $\endgroup$
    – charles
    Jan 3, 2016 at 23:45

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