The approach

No single interpretation method is perfect, and even in the best scenario can only tell you one part of the story. Therefore, you have decided to first assess the model's predictive performance using traditional interpretation methods including the following:

• ROC curves and ROC-AUC
• Confusion matrices and all metrics derived from them (accuracy, precision, recall, F1).

Then, you'll examine the model using two activation-based methods:

• Intermediate activation
• Activation maximization

This is followed by evaluating decisions with three gradient-based methods:

• Saliency maps
• Grad-CAM
• Integrated gradients

This is followed by three perturbation-based methods:

• Occlusion sensitivity
• LIME
• CEM

And, lastly, a bonus backpropagation-based method:

• SHAP's DeepExplainer

I hope that you understand why the model is not performing as it should and how to fix it by the end of this...