How to evaluate the "betterness" of competitive good models?
Lets say I could get good models (> 90% prediction rate) with e.g.:
- F-test based sklearn.feature_selection.f_regression
- mutual info -based sklearn.feature_selection.mutual_info_regression
But since these treat e.g. the independence/dependence of features differently, particularly e.g. LinearSVC assumes "relatively independent" features, where as mutual info particularly measure dependence between variables, then
How can I compare these models to each other?
Tests? Knowledge of the data a priori? Something else?