Previously, we looked at the pitfalls with the default "feature importance" in tree based models, talked about permutation importance, LOOC importance, and Partial Dependence Plots. Now let's switch lanes and look at a few model agnostic techniques which takes a bottom-up way of explaining predictions. Instead of looking at the model and trying to come … Continue reading Interpretability: Cracking open the black box – Part III
Interpretability: Cracking open the black box – Part II
In the last post in the series, we defined what interpretability is and looked at a few interpretable models and the quirks and 'gotchas' in it. Now let's dig deeper into the post-hoc interpretation techniques which is useful when you model itself is not transparent. This resonates with most real world use cases, because whether … Continue reading Interpretability: Cracking open the black box – Part II
Interpretability: Cracking open the black box – Part I
Interpretability is the degree to which a human can understand the cause of a decision - Miller, Tim[1] Explainable AI (XAI) is a sub-field of AI which has been gaining ground in the recent past. And as I machine learning practitioner dealing with customers day in and day out, I can see why. I've been … Continue reading Interpretability: Cracking open the black box – Part I
Deep & Shallow 