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

Understanding classifications with perturbation-based attribution methods

The code for this section alone can be found here: All the preparation steps are repeated from the beginning. However, it has disabled TensorFlow 2 behavior (tf.compat.v1.disable_v2_behavior()) because, at the time of writing, the alibi library, which we will use for the contrastive explanation method, still relies on TensorFlow 1 constructs.

Perturbation-based methods have already been covered to a great extent in this book so far. So many of the methods we have covered, including SHAP, LIME, Anchors, and even Permutation Feature Importance, employ perturbation-based strategies. The intuition behind them is that if you remove, alter, or mask features in your input data and then make predictions with them, you'll be able to attribute the difference between the new predictions...