The evaluation metrics are defined here: ```python examples/mnist/model.py: self.eval_metrics = [accuracy, self.get_criterion()] ``` The required values for them are defined in `src/main.py`: ```python drift_signal = detector.update( value=0.0, # dummy value for base class compatibility modelHarness=modelHarness, reference_validation_metrics=[90, 1.0], higher_is_better=[True, False], ) ``` These metrics/values are used to evaluate the performance of a model in `src/drift_detection/detectors/model_performance_detector.py` This is convoluted and will be hard to understand/debug without proper documentation. I propose we have a list of metrics we support and each example chooses a subset and defines the desired values in their toml files