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

Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter?

We live in a world whose rules and procedures are governed by data and algorithms.

For instance, there are rules as to who gets approved for credit or released on bail, and which social media posts might get censored. There are also procedures to determine which marketing tactics are most effective and which chest x-ray features might diagnose a positive case of pneumonia.

You expect this because it is nothing new!

But not so long ago, rules and procedures such as these used to be hardcoded into software, textbooks, and paper forms, and humans were the ultimate decision-makers. Often, it was entirely up to human discretion. Decisions depended on human discretion because rules and procedures were rigid and, therefore, not always applicable. There were always exceptions, so a human was needed to make them.

For example, if you would ask for a mortgage, your approval depended on an acceptable and reasonably lengthy credit history. This data, in turn, would produce a credit score using a scoring algorithm. Then, the bank had rules that determined what score was good enough for the mortgage you wanted. Your loan officer could follow it or override it.

These days, financial institutions train models on thousands of mortgage outcomes, with dozens of variables. These models can be used to determine the likelihood that you would default on a mortgage with a presumed high accuracy. If there is a loan officer to stamp the approval or denial, it's no longer merely a guideline but an algorithmic decision. How could it be wrong? How could it be right?

Hold on to that thought because, throughout this book, we will be learning the answers to these questions and many more!

To interpret decisions made by a machine learning model is to find meaning in it, but furthermore, you can trace it back to its source and the process that transformed it. This chapter introduces machine learning interpretation and related concepts such as interpretability, explainability, black-box models, and transparency. This chapter provides definitions for these terms to avoid ambiguity and underpins the value of machine learning interpretability. These are the main topics we are going to cover:

  • What is machine learning interpretation?
  • Understanding the difference between interpretation and explainability
  • A business case for interpretability

Let's get started!