Removing columns with low variance I'd like to learn a model from many features.
As a pre-processing step, I will remove constant features.
I'd like to remove almost constant features as well, i.e. I'd like to set a low variance filter.
Would you have any guidance on how to implement this filter?
My concern is that some features are around 100 (in which case a variance of 0.1 is very small) and some other features are around 0 (in which case a variance of 0.1 is not small).

Low variance features could still have a huge impact. There are lots of methods that try to figure out feature importance. Quite popular is for example the usage of random forests, see here.
