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

It has been estimated that there are over 10 million non-profits worldwide, and while a large portion of them have public funding, most of them depend mostly on private donors, both corporate and individual, to continue operations. As such, fundraising is mission-critical and carried out throughout the year.

Year over year, donation revenue has grown but there are several problems non-profits face: donor interests evolve, so a charity popular one year might be forgotten the next; competition is fierce between non-profits; and demographics are shifting. In the United States, the average donor only gives two charitable gifts per year and is over 64 years old. Identifying potential donors is challenging and campaigns to reach them can be expensive.

A National Veterans Organization non-profit arm has a large mailing list of about 190,000 past donors and would like to send a special mailer to ask for donations. However, even with a special bulk discount rate, it costs...