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

The mission

Picture yourself, a data science consultant, in a conference room in Forth Worth, Texas, during early January 2019. In this conference room, executives for one of the world's largest airlines, American Airlines (AA), are briefing you on their on-time performance (OTP). OTP is a widely accepted key performance indicator for flight punctuality. It is measured as the percentage of flights that arrived within 15 minutes of the scheduled arrival. It turns out that AA has achieved an OTP of just over 80% for 3 years in a row, which is already acceptable, and much better than before, but they are still ninth in the world and fifth in North America. To brag about it next year in their advertising, they aspire to achieve, at least, number one in North America for 2019, besting their biggest rivals.

On the financial front, it is estimated that delays cost the airline close to $2 billion, so reducing this by even 25-35% to be on parity with their competitors could produce...