So basically I have a large set of features corresponding to a metric - like many ML problems.

What I want to know is: can we correlate the variance of metric with the variation in each feature.


I have features x, y, z that produce an output say 10. When I vary x, no matter how much I vary it, the output stays relatively close to 10. However, when I vary y the output is heavily influenced.

Is there a good technique to be able to assign a value correlating x and/or y to the metric?

I'm mostly looking for direction here.. i.e. techniques or relevant papers. In my experience I haven't really come across this problem. I don't have a good solution in my toolbelt.



1 Answer 1


If the variation in values of feature then should not you remove the column? Your model will not learn from it if the variance is low.

you can Q-Q plots when you vary the features to check how close are the 2 datasets comparing their distribution

  • $\begingroup$ I'm not actually building a model. The entire purpose is actually exactly what you presumed. That is: to see which features can be removed (due to no variance) and which should be tested more (due to high variance). Thanks, I'll look into Q-Q plots. $\endgroup$
    – waffles
    Dec 20, 2019 at 3:40

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