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

Appreciating what hinders machine learning interpretability

In the last section, we were wondering why the chart with ap_hi versus weight didn't have a conclusive pattern. It could very well be that although weight is a risk factor, there are other critical mediating variables that could explain the increased risk of CVD. A mediating variable is one that influences the strength between the independent and target (dependent) variable. We probably don't have to think too hard to find what is missing. In Chapter 1, Interpretation, Interpretability, and Explainability; and Why Does It All Matter?, we performed linear regression on weight and height because there's a linear relationship between these variables. In the context of human health, weight is not nearly as meaningful without height, so you need to look at both.

Perhaps if we plot the decision regions for these two variables, we will get some clues. We can plot them with the following code:

fig, ax = plt.subplots...