Explore an insightful conference talk on interpretable active learning presented by Richard Phillips at FAT* 2018. Delve into the innovative approach that combines active learning with LIME (Local Interpretable Model-agnostic Explanations) to enhance model interpretability. Discover how this method can improve the efficiency and transparency of machine learning models. Learn about the formula behind this technique and its potential applications in various fields. Gain valuable insights from the presentation's conclusion and engage with the thought-provoking questions raised during the discussion.
Overview
Syllabus
Introduction
Title
Lime
Formula
Conclusion
Questions
Taught by
ACM FAccT Conference
Related Courses
-
Explainable deep learning models for healthcare - CDSS 3
-
Interpretable Machine Learning Applications: Part 2
-
Explainable Machine Learning (XAI)
-
What is Interpretable Machine Learning - ML Explainability - with Python LIME Shap Tutorial
-
Apply LIME to Explain, Trust, and Validate Your Predictions for Any ML Model
-
Just-So Stories for AI - Explaining Black-Box Predictions
