christophM/interpretable-ml-book
A guide book on interpretable machine learning techniques for explaining black box models, written in Jupyter Notebook format.
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This repository hosts a comprehensive book on interpretable machine learning, covering techniques to make black box models transparent and explainable. The book progresses from simple interpretable models to analyzing complex model decisions, targeting practitioners, data scientists, and stakeholders working with ML systems.
Frequently asked
- What is christophM/interpretable-ml-book?
- A guide book on interpretable machine learning techniques for explaining black box models, written in Jupyter Notebook format.
- Is interpretable-ml-book open source?
- Yes — christophM/interpretable-ml-book is an open-source project tracked on heatdrop.
- What language is interpretable-ml-book written in?
- christophM/interpretable-ml-book is primarily written in Jupyter Notebook.
- How popular is interpretable-ml-book?
- christophM/interpretable-ml-book has 5.3k stars on GitHub.
- Where can I find interpretable-ml-book?
- christophM/interpretable-ml-book is on GitHub at https://github.com/christophM/interpretable-ml-book.