Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples, 2nd Edition
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Packt Publishing - ebooks Account
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, Second Edition is the book for you.
Youll cover the fundamentals of interpretability, its relevance in business, and explore its key aspects and challenges.
See how white-box models work, compare them to black-box and glass-box models, and examine their trade-offs. Get 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, tabular data, time-series, images, or text.
In addition to the step-by-step code, this book will also help you interpret model outcomes using many examples. Youll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods youll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. Youll also look under the hood of the latest NLP transformer models using the Language Interpretability Tool.
By the end of this book, you'll understand ML models better and enhance them through interpretability tuning.
- Recognize the importance of interpretability in business
- Study models that are intrinsically interpretable such as linear models, decision trees, Naive Bayes, and glass-box models, such as EBM and Gami-NET
- Become well-versed in interpreting black-box models with model-agnostic methods
- Use monotonic and interaction constraints to make fairer and safer models
- Understand how to mitigate the influence of bias in datasets
- Discover how to make models more reliable with adversarial robustness
- Understand how transformer models work and how to interpret them
- 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
- Anchor and Counterfactual Explanations
- Visualizing Convolutional Neural Networks
- Understanding NLP Transformers
- Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
- Feature Selection and Engineering for Interpretability
- Bias Mitigation and Causal Inference Methods
- Feature Selection for Interpretability
(N.B. Additional chapters to be confirmed upon publication
