公平性:编程练习
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
以下练习演示了如何审核 Google Cloud 中
思维方式,以及如何利用偏见补救策略解决公平性问题
问题:
直接在浏览器中运行编程练习(无需设置
使用 Colaboratory 来运行
平台。大多数主流浏览器都支持 Colaboratory,
在桌面版 Chrome 和 Firefox 上进行了全面测试。
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC):2024-08-13。
[null,null,["最后更新时间 (UTC):2024-08-13。"],[[["\u003cp\u003eThis exercise demonstrates how to audit data sets for fairness and apply bias-remediation strategies.\u003c/p\u003e\n"],["\u003cp\u003eThe programming exercises are run directly in your browser using the Colaboratory platform with no setup required.\u003c/p\u003e\n"],["\u003cp\u003eColaboratory is supported on most major browsers and is most thoroughly tested on desktop versions of Chrome and Firefox.\u003c/p\u003e\n"],["\u003cp\u003eUsers can access a Help Center for support with machine learning education.\u003c/p\u003e\n"]]],[],null,["# Fairness: Programming exercise\n\nThe following exercise demonstrates how to audit data sets with fairness in\nmind, and how to employ bias-remediation strategies to address fairness\nissues: \n[Open fairness exercise](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/fairness_income.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=fairness)\n\nProgramming exercises run directly in your browser (no setup\nrequired!) using the [Colaboratory](https://colab.research.google.com)\nplatform. Colaboratory is supported on most major browsers, and is most\nthoroughly tested on desktop versions of Chrome and Firefox. \n[Help Center](https://support.google.com/machinelearningeducation)"]]