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Interpretable Machine Learning with Python - Second Edition

You're reading from  Interpretable Machine Learning with Python - Second Edition

Product type Book
Published in Oct 2023
Publisher Packt
ISBN-13 9781803235424
Pages 606 pages
Edition 2nd Edition
Languages
Author (1):
Serg Masís Serg Masís
Profile icon Serg Masís

Table of Contents (17) Chapters

Preface Interpretation, Interpretability, and Explainability; and Why Does It All Matter? Key Concepts of Interpretability Interpretation Challenges Global Model-Agnostic Interpretation Methods Local Model-Agnostic Interpretation Methods Anchors and Counterfactual Explanations Visualizing Convolutional Neural Networks Interpreting NLP Transformers Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis Feature Selection and Engineering for Interpretability Bias Mitigation and Causal Inference Methods Monotonic Constraints and Model Tuning for Interpretability Adversarial Robustness What’s Next for Machine Learning Interpretability? Other Books You May Enjoy
Index

The mission

Over 2.8 billion credit cards are circulating worldwide, and we collectively spend over $25 trillion (US) on them every year (https://www.ft.com/content/ad826e32-2ee8-11e9-ba00-0251022932c8). This is an astronomical amount, no doubt, but the credit card industry’s size is best measured not by what is spent, but by what is owed. Card issuers such as banks make the bulk of their money from interest. So, the over $60 trillion owed by consumers (2022), of which credit card debt is a sizable portion, provides a steady income to lenders in the form of interest. It could be argued this is good for business, but it also poses ample risk because if a borrower defaults before the principal plus operation costs have been repaid, the lender could lose money, especially once they’ve exhausted legal avenues to collect the debt.

When there’s a credit bubble, this problem is compounded because an unhealthy level of debt can compromise lenders’ finances...

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