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)
1
Section 1: Introduction to Machine Learning Interpretation
5
Section 2: Mastering Interpretation Methods
12
Section 3:Tuning for Interpretability

Discovering wrapper, hybrid, and advanced feature selection methods

The feature selection methods studied so far are computationally inexpensive because they require no model fitting or fitting simpler white-box models. In this section, we will learn about other, more exhaustive methods with many possible tuning options. The categories of methods included here are as follows:

  • Wrapper: Exhaustively look for the best subset of features by fitting an ML model using a search strategy that measures improvement on a metric.
  • Hybrid: A method that combines embedded and filter methods with wrapper methods.
  • Advanced: A method that doesn't fall into any of the previously discussed categories. Examples include dimensionality reduction, model-agnostic feature importance, and GAs.

And now, let's get started with wrapper methods!

Wrapper methods

The concept behind wrapper methods is reasonably simple: evaluate different subsets of features on the ML model and...