Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models

by Przemyslaw Biecek, Tomasz Burzykowski

Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.

324 pages, Kindle Edition

Published March 17, 2021

Reviews

[Minervas Owl · Rating 4 out of 5]

I was searching "exploratory model analysis" and Google returned this book as the first result, probably because of the similarity in spelling. The book's introduction gave me high hopes, but I don't think it has fully delivered. The chap 15, "model performance measures," is especially a disappointment. But I did find the book's introduction to Shapley values is more intuitive than those I have previously read. It might also be worth trying out the Python package developed by the author, which conveniently implements many interpretive methods, including some I haven't used before, such as grouped PDP plot.

https://pbiecek.github.io/ema/