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

The approach

You have decided to take a three-fold approach, as follows:

  • Placing guardrails with feature engineering: Leveraging lessons learned in Chapter 7, as well as the domain knowledge we already have about priors and age, in particular, we will engineer some features.
  • Tuning models for interpretability: Once the data is ready, we will tune many models with different class weighting and overfitting prevention techniques. These methods will ensure that the models not only generalize better but are easier to interpret.
  • Implementing model constraints: Last but not least, we will implement monotonic and interaction constraints on the best models to make sure that they don't stray from trusted and fair interactions.

In the last two sections, we will make sure the models perform accurately and fairly. We will also compare recidivism risk distributions between the data and the model to ensure that they align.