1- Would it always be beneficial to remove highly correlated features prior to training a model? If not, why not.

2- Would you perform One Hot encoding where applicable, prior to removing highly correlated features ? (taking into account a dummy variable may be highly correlated with another variable)



(1) No. For example, if you have computer vision problems, then each pixel of the image is a feature. most neighboring pixels are highly correlated.

For example, see the following image from The HASYv2 dataset:

enter image description here

  • $\begingroup$ Interesting. How about for classification/regression algorithms such as xgboost, random forest etc ? $\endgroup$
    – Gale
    Mar 6 '18 at 10:52
  • $\begingroup$ I haven't executed a comparative analysis of the performance of different classifiers with vs without the removal of highly correlated features. Only for CNNs on Computer Vision tasks I know for sure that you don't want to remove highly correlated features. $\endgroup$ Mar 6 '18 at 11:37

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