You're reading from Interpretable Machine Learning with Python Build explainable, fair, and robust high-performance models with hands-on, real-world examples

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781803235424

Length 606 pages

Edition 2nd Edition

Languages

Python

Tools

BERT

Concepts

Machine Learning

Author (1):

Serg Masís

View More author details

Table of Contents (17) Chapters

Preface

1. Interpretation, Interpretability, and Explainability; and Why Does It All Matter?

2. Key Concepts of Interpretability FREE CHAPTER

3. Interpretation Challenges

4. Global Model-Agnostic Interpretation Methods

5. Local Model-Agnostic Interpretation Methods

6. Anchors and Counterfactual Explanations

7. Visualizing Convolutional Neural Networks

8. Interpreting NLP Transformers

9. Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

10. Feature Selection and Engineering for Interpretability

11. Bias Mitigation and Causal Inference Methods

12. Monotonic Constraints and Model Tuning for Interpretability

13. Adversarial Robustness

14. What’s Next for Machine Learning Interpretability?

15. Other Books You May Enjoy

16. Index

What is machine learning interpretation?

To interpret something is to explain the meaning of it. In the context of machine learning, that something is an algorithm. More specifically, that algorithm is a mathematical one that takes input data and produces an output, much like with any formula.

Let’s examine the most basic of models, simple linear regression, illustrated in the following formula:

Once fitted to the data, the meaning of this model is that predictions are a weighted sum of the x features with the β coefficients. In this case, there’s only one x feature or predictor variable, and the y variable is typically called the response or target variable. A simple linear regression formula single-handedly explains the transformation, which is performed on the input data x₁ to produce the output . The following example can illustrate this concept in further detail.