Book Image

Interpretable Machine Learning with Python

By : Serg Masís
Book Image

Interpretable Machine Learning with Python

By: Serg Masís

Overview of this book

Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.
Table of Contents (19 chapters)
Section 1: Introduction to Machine Learning Interpretation
Section 2: Mastering Interpretation Methods
Section 3:Tuning for Interpretability

Mission accomplished

The mission was to train a traffic prediction model and understand what factors create uncertainty and possibly increase costs for the construction company. We can conclude that a large portion of the potential $20,000/year in fines can be attributed to the is_holiday factor. Therefore, the construction company should rethink working holidays. There are only five or six holidays between May and October, and they could cost more because of the fines than working on a few Sundays instead. Of course, these conclusions are for the chosen model – which we can compare with other, better models. With this caveat, the mission was successful, but there's still a lot of room for improvement.

For instance, one thing that could be covered in further depth is the true impact of temp and rain_1h, and features for snow. Our prediction approximation method precluded Sobol from testing the effect of extreme weather events. If we modified the model to train on aggregated...