Model Interpretability and Explainability: A Comprehensive Guide

This article discusses techniques and best practices for explaining the predictions made by tree-based, neural network, and deep learning models. As machine learning models become more prevalent in decision-making processes, it is important to understand how these models make predictions and to be able to explain their decision-making process to a wide range of audiences. This is known as model explainability, or the ability to explain the predictions made by a model in a way that is easily understood by humans. Model explainability is important for a number of reasons, including building trust in the model, identifying biases, and improving the model's performance. There are two main categories of model explainability techniques: local explanation techniques and global explanation techniques.