Feature selection- feature that most of its values are equal. How can I know if to drop it? If I have a feature that most of its values are the same, how can I know if to drop it or to keep it?
First of all, most of the model's features have low variance. Secondly, maybe the observations that have different values in this feature get different values in the dependent variable.
Is there any method or heuristic that can help me decide if should I keep this feature?
For example the next feature:


Is there a reason why you would select the features in advance? Otherwise, most likely during the modeling process, you will obtain insight whether to include or drop this feature.
For example, if you apply regularization, lasso (L1) can push some coefficients to 0, so it indirectly does the feature selection for you.
If you use tree based models, it also does the feature selection in a way by splitting on the most relevant features first. In the end, you can remove the features which are identified as less important by such models.
