I'm trying to optimize my features in a dataset to get a better predictive model. I used Exhaustive feature selector from mlxtend. This checks all possible combinations of features. I have a dataset with 80+ features, and I set the maximum of features to be selected to 20 with 10-Fold cross validation. There are 9k entries and the estimator is Random Forest Regressor. It's been roughly three days this has been running. Is this normal? Previously I have run Recursive Feature Elimination on the same dataset with 10 fold cross validation. It took roughly 24 hours.

efs = EFS(RandomForestRegressor(), 

     efs.fit(X, Y)
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    $\begingroup$ yes given the size of dataset and combinations it can be normal $\endgroup$
    – Nikos M.
    Dec 4, 2021 at 19:37
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    $\begingroup$ I had one which ran for a week. You could reduce cv to save some time. $\endgroup$
    – Peter
    Dec 4, 2021 at 21:35

1 Answer 1


An exhaustive search means that the 10-fold CV is run for every possible subset of features. The number of possible subsets of size 20 for a set of 80 features is:

$$\binom{80}{20}=3.5\ 10^{18}$$.

And this is only for size 20 exactly, so the total number is higher.

So yes, it might take a very long time. You could estimate the duration before running a potentially very long experiment like this:

  • First take a lower maximum number of features and calculate the corresponding number of runs. For instance with 5 as maximum the number of subsets would be roughly around 24 millions (I let you calculate the exact number).
  • Then run the experiment and measure the duration. Applying a simple proportion you can estimate how much time it would takes for any maximum number of features. You might find out that your current experiment would take many years ;)

Exhaustive search on a large set is rarely a good idea, I'd suggest looking at genetic search.

  • $\begingroup$ I will do for maximum features of 5, and with no cross validation. Will post how long it took. Would you suggest any particular GA’s and where are they implemented? $\endgroup$ Dec 5, 2021 at 17:42
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    $\begingroup$ @PlatinumMaths I haven't used it myself but there is this library for python. $\endgroup$
    – Erwan
    Dec 5, 2021 at 18:27

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