Masis, Serg
Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples
- ISBN 13:
- 9781800203907
- author:
- Masis, Serg
- format:
- Paperback / softback
- publisher:
- Packt Publishing Limited
- language:
- English
- Publication Year:
- 2021
- Pages:
- 736
- Dimensions:
- 23.5 x 19.1 x 3.7 centimeters (1
- Genre:
- Computers, Special Topics, Expert Systems,
- Condition:
- New
- Availability:
- Item usually sent within 7 working days
Description
Build reliable and fair machine learning models with this comprehensive guide. Interpretable Machine Learning with Python covers the key aspects and challenges of machine learning interpretability, helping you to extract insights from any model and mitigate risks associated with poor predictions. You'll learn how to work effectively with ML models, including white-box, black-box, and glass-box models, and explore a range of interpretation methods, or Explainable AI (XAI) methods. The book also provides hands-on guidance on tuning models and training data for interpretability, covering techniques such as feature selection, dataset debiasing, and monotonic constraints. With this book, you'll gain the skills to build fairer, safer, and more reliable models, reducing the risks associated with AI systems before they have broader implications.
Related Products
Related Products
Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world exa
Masís, Serg
Python Machine Learning by Example
Yuxi
Python Machine Learning By Example - Fourth Edition: Unlock machine learning best practices with real-world use cases
Yuxi
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
Raschka, Sebastian
Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples
McMahon, Andrew P.