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

Tuning models for interpretability

Traditionally, regularization was only achieved by imposing penalty terms such as L1, L2, or Elastic-net on the coefficients or weights, which shrink the impact of the least relevant features. As seen in Embedded methods section of Chapter 10, Feature Selection and Engineering for Interpretability, this form of regularization results in feature selection while also reducing overfitting. And this brings us to another broader definition of regularization, which does not require a penalty term. Often, this comes as imposing a limitation, or a stopping criterion that forces the model to curb its complexity.

In addition to regularization, both in its narrow (penalty-based) and broad sense (overfitting methods), there are other methods that tune a model for interpretability—that is, improve the fairness, accountability, and transparency of a model through adjustments to the training process. For instance, the class imbalance hyperparameters we...