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AstraZeneca avatar

AstraZeneca/rexmex

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278 星标·25 分支·Python·9 次浏览rexmex.readthedocs.io↗

Rexmex

A general purpose recommender metrics library for fair evaluation.

Features

  • General Machine Learning - Library for evaluating recommender system metrics.
  • Data Processing and Analysis - Metrics library for recommender system evaluation.
  • Data Science Tooling - Metrics library for recommender system evaluation.
  • Data Science Tools - Metrics library for evaluating recommender systems.

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常见问题解答

astrazeneca/rexmex 是做什么的?

A general purpose recommender metrics library for fair evaluation.

astrazeneca/rexmex 的主要功能有哪些?

astrazeneca/rexmex 的主要功能包括:General Machine Learning, Data Processing and Analysis, Data Science Tooling, Data Science Tools。

astrazeneca/rexmex 有哪些开源替代品?

astrazeneca/rexmex 的开源替代品包括: cleanlab/cleanlab — Cleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting… astrazeneca/chemicalx — A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022). benedekrozemberczki/karateclub — Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020). benedekrozemberczki/littleballoffur — Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020). adrotog/pandasgui — A GUI for Pandas DataFrames. alteryx/featuretools — Featuretools is an automated feature engineering library and data transformation framework written in Python. It…

Rexmex 的开源替代方案

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    benedekrozemberczki/karateclub

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    Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

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    Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

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  • astrazeneca/chemicalxAstraZeneca 的头像

    AstraZeneca/chemicalx

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    A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

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  • cleanlab/cleanlabcleanlab 的头像

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    Cleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting label errors and increasing overall dataset quality. It implements a confident learning framework that iteratively refines label noise estimates by comparing model predictions with estimated label probabilities to identify mislabeled examples. The project provides specialized utilities for active learning optimization, allowing for the selection of the most impactful examples for labeling or re-labeling. It also includes an outlier detection tool to identify atypical data poin

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