I have a random forest model that attempts to classify a binary outcome (either "Good" or "Bad"). I am currently looking at ways in which I can evaluate the performance of the model when it is used on newer data to determine if the model is still performing acceptably.
The only performance metrics I can think off are:
- Comparison of average actual vs predicted "bad rate"
Are there any other tests that are used to monitor how well a random forest model is performing?
Note: In another question ( Random Forest - Variable Importance over time ) I tried asking about how to explain the predictions of a random forest model. However, this question is not about model interpretability. My main goal here is to find a set of performance metrics that can help determine whether my model needs to be re-trained or not.