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[PR #282] [MERGED] Added the Shapley framework #290

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opened 2025-11-06 17:16:36 -06:00 by GiteaMirror · 0 comments
GiteaMirror commented 2025-11-06 17:16:36 -06:00
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📋 Pull Request Information

Original PR: https://github.com/oxnr/awesome-bigdata/pull/282
Author: @benedekrozemberczki
Created: 1/1/2021
Status: ✅ Merged
Merged: 1/25/2021
Merged by: @oxnr

Base: master ← Head: patch-1

📝 Commits (1)

  • 7d31b6f Added the Shapley framework

📊 Changes

1 file changed (+1 additions, -0 deletions)

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📝 README.md (+1 -0)

📄 Description

Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble.

The library consists of various methods to compute (approximate) the Shapley value of players (models) in weighted voting games (ensemble games). We covered widely know approximation methods from well known economics and computer science research papers. There are also functionalities to identify the heterogeneity of the player pool. In addition, the framework comes with a detailed documentation, 100% test coverage and illustrative toy examples.

🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.

## 📋 Pull Request Information **Original PR:** https://github.com/oxnr/awesome-bigdata/pull/282 **Author:** [@benedekrozemberczki](https://github.com/benedekrozemberczki) **Created:** 1/1/2021 **Status:** ✅ Merged **Merged:** 1/25/2021 **Merged by:** [@oxnr](https://github.com/oxnr) **Base:** `master` ← **Head:** `patch-1` --- ### 📝 Commits (1) - [`7d31b6f`](https://github.com/oxnr/awesome-bigdata/commit/7d31b6f5c9ff564d60c6c875cb3a3658fe0e7f43) Added the Shapley framework ### 📊 Changes **1 file changed** (+1 additions, -0 deletions) <details> <summary>View changed files</summary> 📝 `README.md` (+1 -0) </details> ### 📄 Description Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The library consists of various methods to compute (approximate) the Shapley value of players (models) in weighted voting games (ensemble games). We covered widely know approximation methods from well known economics and computer science research papers. There are also functionalities to identify the heterogeneity of the player pool. In addition, the framework comes with a detailed documentation, 100% test coverage and illustrative toy examples. --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
GiteaMirror added the pull-request label 2025-11-06 17:16:36 -06:00
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Reference: github-starred/awesome-bigdata#290
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