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

Detecting bias

There are many sources for bias in machine learning. As outlined in Chapter 1, Interpretation, Interpretability, and Explainability; and Why Does It All Matter?, there are ample sources of bias. Those rooted in the truths that the data is representing, such as systemic and structural ones that lead to prejudice bias in the data. There are also biases rooted in the data itself, such as sample, exclusion, association, and measurement biases. Lastly, there are biases in the insights we derive from data or models we have to be careful with, such as conservatism bias, salience bias, and fundamental attribution error.

For this example, to properly disentangle so many bias levels, we ought to connect our data to census data for Taiwan in 2005 and historical lending data split by demographics. Then, using these external datasets, control for credit card contract conditions, as well as gender, income, and other demographic data to ascertain if young people, in particular,...