From 0576c0fdc134ed372d0425be182e1b565b02bac8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tomomi=20=E2=9D=A4=20Imura?= Date: Tue, 1 Jun 2021 13:59:05 -0700 Subject: [PATCH] Fix broken link --- Introduction/3-fairness/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Introduction/3-fairness/README.md b/Introduction/3-fairness/README.md index eb8626d2..7e625afe 100644 --- a/Introduction/3-fairness/README.md +++ b/Introduction/3-fairness/README.md @@ -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).