AI News, Explaining behavior of Machine Learning models with eli5 library

Explaining behavior of Machine Learning models with eli5 library

Target audience is ML practitioners who want to 1) get a better quality from their ML pipelines - understanding of why a wrong decision happens is often a first step to improve the quality of an ML solution; 2)

humans trust such models more because they can check if an explanation is consistent with their domain knowledge or gut feeling, understand better shortcomings of the solution and make a more informed decision as a result.

Target audience is ML practitioners who want to 1) get a better quality from their ML pipelines - understanding of why a wrong decision happens is often a first step to improve the quality of an ML solution;

humans trust such models more because they can check if an explanation is consistent with their domain knowledge or gut feeling, understand better shortcomings of the solution and make a more informed decision as a result.

Mikhail Korobov - Explaining behavior of Machine Learning models with eli5 library

"Explaining behavior of Machine Learning models with eli5 library [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] ML estimators don't have to ...

Visualizing a Decision Tree - Machine Learning Recipes #2

Last episode, we treated our Decision Tree as a blackbox. In this episode, we'll build one on a real dataset, add code to visualize it, and practice reading it - so ...

Finale Doshi-Velez: "A Roadmap for the Rigorous Science of Interpretability" | Talks at Google

With a growing interest in interpretability, there is an increasing need to characterize what exactly we mean by it and how to sensibly compare the interpretability ...

Deep Attention Mechanism for Multimodal Intelligence: Perception, Reasoning, & Expression

We have long envisioned that machines one day can perform human-like perception, reasoning, and expression across multiple modalities including vision and ...