Masís, Serg
Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world exa
- ISBN 13:
- 9781803235424
- author:
- Masís, Serg
- format:
- Paperback
- publisher:
- Packt Publishing
- language:
- English
- Publication Year:
- 2023
- Pages:
- 606
- Dimensions:
- 23.5 x 19.1 x 3.1 centimetres (1
- Genre:
- Computers, Special Topics, Expert Systems,
- Condition:
- New
- Availability:
- Item usually sent within 7 working days
Description
Deepen your understanding of machine learning with this comprehensive guide to interpretability. Learn how to build fairer, safer, and more reliable models using SHAP, feature importance, and causal inference. With real-world data examples from cardiovascular disease and COMPAS recidivism scores, you'll gain hands-on experience in analyzing complex models, including CNNs, BERT, and time series models. The book introduces a range of techniques, such as traditional methods like partial dependence plots and integrated gradients, as well as more advanced approaches like gradient-based attribution methods. By the end of this book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.
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