Lecture 8 - Explainable AI on tabular data using LIME | Colab implementation | Trees + Random forest

Welcome to the Lecture on LIME in Explainable AI. Let us learn how to implement LIME for tabular data to analyze which features (columns) contributed the most towards an ML prediction. ================================================= References: Paper: The original LIME paper- https://arxiv.org/abs/1602.04938 GitHub repo: https://github.com/marcotcr/lime Dataset: https://www.kaggle.com/code/adhamtare... Google Colab Notebook: https://colab.research.google.com/dri... ✉️ Join our FREE Newsletter: https://vizuara.ai/our-newsletter/ ================================================= Explainable AI (XAI) Lecture series is a project started by the co-founders of Vizuara: Dr. Raj Dandekar (IIT Madras Btech, MIT PhD), Dr. Rajat Dandekar (IIT Madras Mtech, Purdue PhD) and Dr. Sreedath Panat (IIT Madras Mtech, MIT PhD). Explainable AI (XAI) lecture series is not a normal video course. In this project, we will teach XAI from scratch. We will make lecture notes, and also share reference material. As we learn the material again, we will share thoughts on what is actually useful in industry and what has become irrelevant. We will also share a lot of information on which subject contains open areas of research. Interested students can also start their research journey there. Students who are confused or stuck in their ML journey, maybe courses and offline videos are not inspiring enough. What might inspire you is if you see someone else learning machine learning from scratch. No cost. No hidden charges. Pure old school teaching and learning. ================================================= 🌟 Meet Our Team: 🌟 🎓 Dr. Raj Dandekar (MIT PhD, IIT Madras department topper) 🔗 LinkedIn:   / raj-abhijit-dandekar-67a33118a   🎓 Dr. Rajat Dandekar (Purdue PhD, IIT Madras department gold medalist) 🔗 LinkedIn:   / rajat-dandekar-901324b1   🎓 Dr. Sreedath Panat (MIT PhD, IIT Madras department gold medalist) 🔗 LinkedIn:   / sreedath-panat