Interpretable vs Explainable Machine Learning

Introduction to Explainable AI (XAI) | Interpretable models, agnostic methods, counterfactuals

The 6 Benefits of Explainable AI (XAI) | Improve accuracy, decrease harm and tell better stories

Get more out of Explainable AI (XAI): 10 Tips

Explaining Machine Learning to a Non-technical Audience

Modelling Non-linear Relationships with Regression

Interpretable Feature Engineering | How to Build Intuitive Machine Learning Features

8 Characteristics of a Good Machine Learning Feature | Predictive, Variety, Interpretability, Ethics

Feature Selection using Hierarchical Clustering | Python Tutorial

8 Plots for Explaining Linear Regression | Residuals, Weight, Effect & SHAP

Permutation Feature Importance from Scratch | Explanation & Python Code

Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math

PDPs and ICE Plots | Python Code | scikit-learn Package

Accumulated Local Effect Plots (ALEs) | Explanation & Python Code

Friedman's H-statistic for Analysing Interactions | Maths and Intuition

Friedman's H-statistic Python Tutorial | Artemis Package

An introduction to LIME for local interpretations | Intuition and Algorithm |

Applying LIME with Python | Local & Global Interpretations