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@scikit-learn @pydataberlin @conda-forge @scikit-learn-contrib @opt-out-tools

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  1. scikit-learn: machine learning in Python

    Python 41.3k 20k

  2. A Python package to assess and improve fairness of machine learning models.

    Python 345 69

  3. A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

    Python 973 318

  4. An Open Source Machine Learning Framework for Everyone

    C++ 146k 81.8k

  5. Models and examples built with TensorFlow

    Python 64.6k 41.3k

  6. PipelineAI Kubeflow Distribution

    Jsonnet 4k 963

758 contributions in the last year

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Contribution activity

June 2020

Created an issue in scikit-learn/scikit-learn that received 10 comments

API split "model" parameters from "config" params.

We can think of our estimators' parameters as two different types: model hyper-parameters which influence the end result, accuracy, complexity, et…

10 comments
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