I have about 100 data sets with over 100,000 rows and 27 predictor variables and multiple response variable on each dataset. I want to select the most essential Features for these datasets. I used Boruta() from the package Boruta, but it takes an extremely long time (about 3 hours for a single sample). Could someone kindly tell me the quickest technique to select key features from a large dataset?

This is the extension from my previous questions:

  1. https://stackoverflow.com/questions/76824529/how-to-do-regression-modelling-in-r-multiple-one-at-a-time-to-multiple-column
  2. How to get coefficient tables from multiple regression model result!
  • $\begingroup$ Your second question has nothing to do with machine learning, it's a simple R question - you ran into problem because you did not use the apply family of functions properly. $\endgroup$
    – Ram RS
    Aug 16 at 19:39
  • $\begingroup$ Right, I thought that too. Really an analyst uses either ML or GLM - not both. Within ML it's common to compare different methods but not move outside ML (unless they want to do deep learning). Its possible "linear regression" ML might be intended. ML is very, very different to GLM, i.e. the method and system to test model. $\endgroup$
    – M__
    Aug 16 at 20:06

1 Answer 1


Feature selection: the fastest thing moving (i.e. the simplest) is naive Bayes ML. I don't use it but KNN (K-nearest neighbor) I do use, which is also computationally simple, i.e. fast. I think it gives better results, i.e. the best results for a simple algorithm.

R ML has improved decision trees BTW - quite popular, but you'd never use this in Python.


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