Interpretable Machine Learning with Python

Interpretable Machine Learning with Python

Book Description
Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models

Key Features

  • Learn how to extract easy-to-understand insights from any machine learning model
  • Become well-versed with interpretability techniques to build fairer, safer, and more reliable models
  • Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models

Book Description

Do you want to understand your models and mitigate the risks associated with poor predictions using practical machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you overcome these challenges, using interpretation methods to build fairer and safer ML models.
The first section of the book is a beginner's guide to interpretability and starts by recognizing its relevance in business and exploring its key aspects and challenges. You’ll focus on how white-box models work, compare them to black-box and glass-box models, and examine the trade-offs. The second section will get you up to speed with interpretation methods and how to apply them to different use cases. In addition to the step-by-step code, there's a strong focus on interpreting model outcomes in the context of each chapter's example. In the third section, you’ll focus on 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 machine learning Python book, you’ll be able to understand ML models better and enhance them through interpretability tuning.

What you will learn

  • Recognize the importance of interpretability in business
  • Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes
  • Become well-versed in interpreting models with model-agnostic methods
  • Visualize how an image classifier works and what it learns
  • Understand how to mitigate the influence of bias in datasets
  • Discover how to make models more reliable with adversarial robustness
  • Use monotonic constraints to make fairer and safer models

Who This Book Is For

This book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.


Section 1: Introduction to Machine Learning Interpretation
Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter?
Chapter 2: Key Concepts of Interpretability
Chapter 3: Interpretation Challenges

Section 2: Mastering Interpretation Methods
Chapter 4: Fundamentals of Feature Importance and Impact
Chapter 5: Global Model-Agnostic Interpretation Methods
Chapter 6: Local Model-Agnostic Interpretation Methods
Chapter 7: Anchor and Counterfactual Explanations
Chapter 8: Visualizing Convolutional Neural Networks
Chapter 9: Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

Section 3: Tuning for Interpretability
Chapter 10: Feature Selection and Engineering for Interpretability
Chapter 11: Bias Mitigation and Causal Inference Methods
Chapter 12: Monotonic Constraints and Model Tuning for Interpretability
Chapter 13: Adversarial Robustness
Chapter 14: What's Next for Machine Learning Interpretability?

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