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I have benn working so so much with explainability. I think the main thing to know when trying to extend this to non vision (which I assume to be standard featu
by lysecret 5y ago
I have benn working so so much with explainability. I think the main thing to know when trying to extend this to non vision (which I assume to be standard feature engineering based ml, let's ignore texts for a second) is that all the explainability is in the features. You have to design features that make sense. It really doesn't matter if you have the best explainability method in the world if that perfect explainability method retrasces the models prediction to features nobody understands.
- raghavbali 5y agoWell, I agree to your point that the explainability is only as good as the features used in the model. Yet it is important to attribute the correct set of features (along with quantification) for a given instance. The SHAP framework does that to a good extent but it's focus is not towards helping with identification of model parameter related issues. This work here seems to focus on the later and hence I found it a bit different and a refreshing perspective towards the explainability aspect.