interpret
AI model explainer
An open-source package for explaining machine learning models and promoting transparency in AI decision-making
Fit interpretable models. Explain blackbox machine learning.
6k stars
146 watching
736 forks
Language: C++
last commit: about 23 hours ago
Linked from 4 awesome lists
aiartificial-intelligencebiasblackboxdifferential-privacyexplainabilityexplainable-aiexplainable-mlgradient-boostingimlinterpretabilityinterpretable-aiinterpretable-machine-learninginterpretable-mlinterpretmlmachine-learningscikit-learntransparencyxai
Related projects:
Repository | Description | Stars |
---|---|---|
| Teaching software developers how to build transparent and explainable machine learning models using Python | 673 |
| Provides counterfactual explanations for machine learning models to facilitate interpretability and understanding. | 1,373 |
| A comprehensive resource for explaining the decisions and behavior of machine learning models. | 4,811 |
| An interactive tool for analyzing and understanding machine learning models | 3,500 |
| Provides tools and techniques for interpreting machine learning models | 483 |
| A framework to explain and debug blackbox machine learning models with a single line of code. | 419 |
| An open-source package that provides interpretable machine learning models compatible with scikit-learn. | 1,406 |
| Provides tools to understand and interpret the decisions made by XGBoost models in machine learning | 253 |
| A Python toolbox for developing and diagnosing interpretable machine learning models with low-code and high-code APIs. | 1,221 |
| An eXplainability toolbox for machine learning that enables data analysis and model evaluation to mitigate biases and improve performance | 1,135 |
| A Python library for building interactive dashboards to explain machine learning models | 2,321 |
| A tool to help understand and explain the behavior of complex machine learning models | 1,390 |
| An implementation of a method to interpret ensemble models by learning compact representations from them | 8 |
| This project provides tools to induce rules from trained neural networks to explain model predictions and data patterns. | 21 |
| An exploratory tool for analyzing and understanding machine learning models | 14 |