awesome-shapley-value
shap
| awesome-shapley-value | shap | |
|---|---|---|
| 1 | 1 | |
| 133 | 20,121 | |
| 4.5% | - | |
| 3.2 | 10.0 | |
| almost 2 years ago | 8 months ago | |
| Jupyter Notebook | ||
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-shapley-value
shap
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Ethical and Bias Testing in Generative AI: A Practical Guide to Ensuring Ethical Conduct with Test Cases and Tools
Other tools like Fairness Indicators, Lime, and SHAP are also valuable resources for ethical and bias testing.
What are some alternatives?
responsible-ai-toolbox - Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
csgo-impact-rating - A probabilistic player rating system for Counter Strike: Global Offensive, powered by machine learning
AIX360 - Interpretability and explainability of data and machine learning models
transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
lime - Lime: Explaining the predictions of any machine learning classifier
DALEX - moDel Agnostic Language for Exploration and eXplanation
augmented-interpretable-models - Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
interpret - Fit interpretable models. Explain blackbox machine learning.
shap - A game theoretic approach to explain the output of any machine learning model.
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
ML-Prediction-LoL - In this project I implemented two machine learning algorithms to predicts the outcome of a League of Legends game.