Deep Learning and Information Theory

If you have tried to understand the maths behind machine learning, including deep learning, you would have come across topics from Information Theory - Entropy, Cross Entropy, KL Divergence, etc. The concepts from information theory is ever prevalent in the realm of machine learning, right from the splitting criteria of a Decision Tree to loss … Continue reading Deep Learning and Information Theory

Interpretability: Cracking open the black box – Part III

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