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 understand why one of your client's bars is Outstanding while another one is Disappointing. Your approach employed the interpretation of machine learning models to arrive at the following conclusions:

  • According to SHAP on the tabular model, the Outstanding bar owes that rating to its berry taste and its cocoa percentage of 70%. On the other hand, the unfavorable rating for the Disappointing bar is due mostly to its earthy flavor and bean country of origin (Other). Review date plays a smaller role, but it seems that chocolate bars reviewed in that period (2013-15) were at an advantage.
  • LIME confirms that cocoa_percent<=70 is a desirable property, and that, in addition to berry, creamy, cocoa, and rich are favorable tastes, while sweet, sour, and molasses are unfavorable.
  • The commonality between both methods using the tabular model is that despite the many non-taste-related attributes, taste features are among the most...