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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 approach

Upon careful consideration, you have decided to approach this both as a regression problem and a classification problem. Therefore, you will produce models that predict minutes delayed as well as models that classify whether flights were delayed by more than 15 minutes. For interpretation, using both will enable you to use a wider variety of methods and expand your interpretation accordingly. So we will approach this example by taking the following steps:

  1. Predicting minutes delayed with various regression methods
  2. Classifying flights as delayed or not delayed with various classification methods

These steps in the Reviewing traditional model interpretation methods section are followed by conclusions spread out in the rest of the sections of this chapter.

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