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

Studying intrinsically interpretable (white-box) models

So far, in this chapter, we have already fitted our training data to model classes representing each of these "white-box" model families. The purpose of this section is to show you exactly why they are intrinsically interpretable. We'll do so by employing the models that were previously fitted.

Generalized Linear Models (GLMs)

GLMs are a large family of model classes that have a model for every statistical distribution. Just like linear regression assumes your target feature and residuals have a normal distribution, logistic regression assumes the Bernoulli distribution. There are GLMs for every distribution, such as Poisson regression for Poisson distribution and multinomial response for multinomial distribution. You choose which GLM to use based on the distribution of your target variable and whether your data meets the other assumptions of the GLM (they vary). In addition to an underlying distribution...