xplainable VS awesome-shapley-value

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

xplainable

Real-time explainable machine learning for business optimisation (by xplainable)
auto-ml data-analytics Data Science explainable-ai explainable-ml Machine Learning machine-learning-algorithms predictions Python Shap Statistics xai xplainable Prediction
Source Code
xplainable.io
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awesome-shapley-value

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022) (by AstraZeneca)
Artificial intelligence Deep Learning Machine Learning Shapley shapley-additive-explanations shapley-value xai Data Science explainability explainable lime Shap explainable-ai explainable-artificial-intelligence explainable-ml shapley-decomposition shapley-q-value owen-value
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xplainable awesome-shapley-value
2 1
52 133
- 4.5%
9.0 3.2
14 days ago almost 2 years ago
Python
GNU Affero General Public License v3.0 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.

xplainable

Posts with mentions or reviews of xplainable. We have used some of these posts to build our list of alternatives and similar projects.
  • Explainable (Structured) Machine Learning Algorithm
    1 project | /r/Python | 5 Dec 2023
    Just for some respite from the discussion of our soon-to-be AI overlords (LLMs), I'm one of the contributors to an open-source Python package, Xplainable (https://github.com/xplainable/xplainable). Xplainable is a novel (structured) machine learning algorithm that's inherently explainable, as opposed to being a post-hoc explainer (like SHAP or Lime).
  • Tools for documenting OS Python Package
    1 project | /r/Python | 20 Nov 2023
    I'm looking at migrating the docs for our open-source Python package https://github.com/xplainable/xplainable from sphinx to something else. I was initially looking at either docosaurus or Mintlify. Mintlify looks substantially easier to setup but I'm questioning the extensibility (also the cost).

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 xplainable and awesome-shapley-value you can also consider the following projects:

statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.

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.

AIX360 - Interpretability and explainability of data and machine learning models

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

shap - A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap]

DALEX - moDel Agnostic Language for Exploration and eXplanation

interpret - Fit interpretable models. Explain blackbox machine learning.

xplainable vs statsforecast awesome-shapley-value vs responsible-ai-toolbox awesome-shapley-value vs AIX360 awesome-shapley-value vs shapley awesome-shapley-value vs shap awesome-shapley-value vs DALEX awesome-shapley-value vs interpret