Data centric Explainable ML Tutorial 6: Lab 2 -Explainable ML using the DALEX Package

This lab explores the basics of the DALEX package to explain the random forest (RF) model for land cover mapping. We will calculate global feature (predictor variable) importance, compute and plot accumulated dependence profile (local effects), calculate Shapley values, and plot break down profiles. Additional resources Data-centric Explainable Machine Learning for Land Cover Classification: A Practical Guide in R https://aigeolabs.com/buy-ebook/ Explainable Machine Learning for Land Cover Classification: An Introductory Guide https://aigeolabs.com/ebooks/ Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. With examples in R and Python. https://ema.drwhy.ai/ R Script for Lab 2 https://aigeolabs.com/wp-content/uploads/2022/03/Lab-2-Explainable-ML-_DALEX.zip Lab 2 Data Set (Download Links) https://aigeolabs.com/wp-content/uploads/2021/09/Gweru_Data_Blog.zip