Determining whether a Machine Learning model is overfitted with regard to the stability of the features

Question

I need to know how would I get to know if I have overfitted my Machine Learning model on the train data. The performance metric I have used is Logistic Loss. Does the stability of the features affect the performance of my model? If yes, how do they relate?

Answer 1

You need to look for the differences in the training loss and the cross-validation & test losses. If those are low, it means the model performs fairly well. Ideally, train loss should be roughly equal to the cross-validation and test losses. If not, the model may be overfitted.

This difference also hints at an insignificant overlap between the train data points and the cross-validation and test data points. Such features are said to be unstable. In such a case, the model only gets to see the data points in the train data and not those in the cross-validation and test data, and is simply overfitted thereby. It, hence, performs poorly. You can verify the same by computing the percentage of data points present in the cross-validation and test data from those in the train data for different features in your dataset.