1 रिपॉजिटरी
Awesome GitHub RepositoriesInterpretability-Based Debugging
Identifying model errors or shortcuts by comparing predictions against domain knowledge.
Distinct from Model Selection and Validation: Distinct from general model selection and validation by focusing on identifying 'shortcuts' via domain knowledge.
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Awesome Interpretability-Based Debugging GitHub Repositories
AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
- christophm/interpretable-ml-book
christophM/interpretable-ml-book
5,317GitHub पर देखेंThis project is a comprehensive educational resource and technical manual focused on interpretable machine learning and explainable AI. It serves as a textbook and reference for implementing techniques that make complex machine learning models transparent and understandable to humans. The resource provides guidance on both building inherently transparent models, such as decision trees and sparse linear models, and applying post-hoc explanation methods to black-box systems. It details specific methodologies for quantifying feature importance, generating rationales for individual predictions, a
Identifies shortcuts or errors in model behavior by comparing predictions against domain knowledge and data distributions.
Jupyter Notebook
