Book Image

Interpretable Machine Learning with Python - Second Edition

By : Serg Masís
4 (4)
Book Image

Interpretable Machine Learning with Python - Second Edition

4 (4)
By: Serg Masís

Overview of this book

Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.
Table of Contents (17 chapters)
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Interpretable Machine Learning with Python, Second Edition: Build Your Own Interpretable Models

Welcome to Packt Early Access. We’re giving you an exclusive preview of this book before it goes on sale. It can take many months to write a book, but our authors have cutting-edge information to share with you today. Early Access gives you an insight into the latest developments by making chapter drafts available. The chapters may be a little rough around the edges right now, but our authors will update them over time.

You can dip in and out of this book or follow along from start to finish; Early Access is designed to be flexible. We hope you enjoy getting to know more about the process of writing a Packt book.

  1. Chapter 1: Interpretation, Interpretability and Explainability; and why does it all matter?
  2. Chapter 2: Key Concepts of Interpretability
  3. Chapter 3: Interpretation Challenges
  4. Chapter 4: Global Model-agnostic Interpretation Methods
  5. Chapter 5: Local...