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 models that could predict preventable delays with enough accuracy to be useful, and thhen, to understand the factors that impacted these delays, according to these models, to improve OTP. The resulting regression models all predicted delays, on average, well below the 15-minute threshold according to the RMSE. And most of the classification models achieved an F1 score well above 50% – one of them reached 98.8%! We also managed to find factors that impacted delays for all white-box models, some of which performed reasonably well. So, it seems like it was a resounding success!

Don't celebrate just yet! Despite the high metrics, this mission was a failure. Through interpretation methods, we realized that the models were accurate mostly for the wrong reasons. This realization helps underpin the mission-critical lesson that a model can easily be right for the wrong reasons, so the question "why?" is not a question to...