Almost in every ML models with high dimensional, one of the first things to do is removing features with low variance in order to decrease dimension.
But, when we do this, we don't examine the correlation between target variable and the feature. What if the correlation between target and feature is very high?
The thing we should do is firstly looking the correlation and then variance? Or is there any reason for removing the low variance first or solution related to this issue that I missed?