shap VS awesome-shapley-value

Compare shap vs awesome-shapley-value and see what are their differences.

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shap awesome-shapley-value
1 1
20,121 133
- 4.5%
10.0 3.2
8 months ago almost 2 years ago
Jupyter Notebook
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

shap

Posts with mentions or reviews of shap. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-18.

awesome-shapley-value

Posts with mentions or reviews of awesome-shapley-value. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing shap and awesome-shapley-value you can also consider the following projects:

csgo-impact-rating - A probabilistic player rating system for Counter Strike: Global Offensive, powered by machine learning

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.

transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

AIX360 - Interpretability and explainability of data and machine learning models

lime - Lime: Explaining the predictions of any machine learning classifier

shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

augmented-interpretable-models - Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.

DALEX - moDel Agnostic Language for Exploration and eXplanation

shap - A game theoretic approach to explain the output of any machine learning model.

interpret - Fit interpretable models. Explain blackbox machine learning.

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.