Based on my limited understanding, there are in general two reasons why feature selection is used:
- Reducing the number of features, to reduce overfitting and improve the generalisation of models.
- To gain a better understanding of the features and their relationship to the response variables.
In most of the articles about feature selection focus is on reducing noise and to find a balance between bias and variance.
My question is - Are there any cases where higher noise(not so important features) in model will actually be helpful?