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

Understanding the effect of irrelevant features

Feature selection is also known as variable or attribute selection. It is the method by which you can automatically or manually select a subset of specific features useful to the construction of ML models.

It's not necessarily true that more features lead to better models. Irrelevant features can impact the learning process, leading to overfitting. Therefore, we need some strategies to remove any features that might adversely affect learning. Some of the advantages of selecting a smaller subset of features include the following:

  • It's easier to understand simpler models: For instance, feature importance for a model that uses 15 variables is much easier to grasp than one that uses 150 variables.
  • Shorter training time: Reducing the number of variables decreases the cost of computing, speeds up model training, and perhaps most notably, simpler models have quicker inference times.
  • Improved generalization by reducing...