Finding out why your model is doing better? I fitted a logistic regression model on a data set and got an AUC score of .70.
I added some additional out-hot encoded categorical features to the model and the AUC improved slightly to .74.
How do I assess how the model improved? What plots/other analyses are used to to assess the performance gain?
I understand that the model improved, but I want to be able to explain why adding those features improved the model.
This is just a general data science question.
Thanks!
First, it makes sense that adding features will improve your performance, just make sure you do evaluation carefully and not overuse the same validation dataset (and if yes try to re evaluate it on an unseen independent different test set) to ensure you are not overfitted.
After that, you can use Shapley values in its aggregate look to see which features (or specific values in your encoding) impacted the model decision making