1. Abstract

Abstract of the book

This book is about building and evaluating the interpretable machine learning model. Starting with introductions and explanations to Interpretable Models, the book also provides the definitions and methods, including Model-specific methods and model-agnostic methods. Moreover, we introduce a structure of an evaluation system for evaluating the interpretable models. The evaluation systems are built up by three main metrics and features that are helpful to the evaluation. This system is not only help to evaluate the interpretable ml model afterward, but more importantly, it can help the user build the model targeted the features that they preferred. Furthermore, a more detailed explanation is presented to declare about why a user would like to choose some specific features and how they do that. An example including the choice of methods as well as the interpretable machine learning notebook is presented at the end of the book.

This is a research idea and following observation and experiment will happen in the future. The future topic will focus on the refinement and standardization of the Evaluation System in order to provide a better interpretable machine learning model.