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Tomomi ❤ Imura 4 years ago committed by GitHub
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@ -151,7 +151,7 @@ This intro lesson does not dive deeply into the details of algorithmic unfairnes
### Fairlearn
\[Fairlearn\](https://fairlearn.github.io/) is an open-source Python package that allows you to assess your systems' fairness and mitigate unfairness.
[Fairlearn](https://fairlearn.github.io/) is an open-source Python package that allows you to assess your systems' fairness and mitigate unfairness.
The tool may help you to assesses how a model's predictions affect different groups, enables comparing multiple models by using fairness and performance metrics, and supply a set of algorithms to mitigate unfairness in binary classification and regression.
- Learn how to use the different components by checking out the Fairlearn's [GitHub](https://github.com/fairlearn/fairlearn/), [user guide](https://fairlearn.github.io/main/user_guide/index.html), [examples](https://fairlearn.github.io/main/auto_examples/index.html), and [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks).

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