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)
1
Section 1: Introduction to Machine Learning Interpretation
5
Section 2: Mastering Interpretation Methods
12
Section 3:Tuning for Interpretability

The mission

The privately contracted security-services-industry market worldwide is valued at over 250 billion United States dollars (USD) and is growing at around 5% annually. However, it faces many challenges, such as a shortage of adequately trained guards and specialized security experts in many jurisdictions, as well as a whole host of unexpected security threats. These threats include widespread coordinated cybersecurity attacks, massive riots, social upheaval, and—last but not least—health risks brought on by pandemics. Indeed, 2020 tested the industry with a wave of ransomware and misinformation attacks, protests, and COVID-19, to boot.

In the wake of this, one of the largest hospital networks in the US asked their contracted security company to monitor the correct use of masks of both visitors and personnel throughout the hospital. The security company struggled with this request because it would divert security personnel from tackling other threats such as...