摘要
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。
从机器学习的角度构建问题是一个两步流程：
通过执行以下操作来验证机器学习是否是一种好的方法：
通过执行以下操作，以机器学习术语来描述问题：
- 定义理想结果和模型的目标。
- 确定模型的输出。
- 定义成功指标。
通过设定明确的目标并提供与机器学习从业者协作的共享框架，这些步骤可以节省时间和资源。
请完成以下练习，以确定机器学习问题并制定解决方案：
Responsible AI
在实施机器学习解决方案时，请务必遵循 Google 的 Responsible AI 原则。
如需获取有关如何通过实践来提高机器学习公平性和缓解偏差的入门介绍，请参阅 MLCC 公平性模块。
不断改进
更多机器学习学习资源
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)：2025-08-04。
[null,null,["最后更新时间 (UTC)：2025-08-04。"],[[["\u003cp\u003eFraming a Machine Learning (ML) problem involves understanding the problem, identifying a use case, understanding the data, and then defining the desired outcome, model output, and success metrics.\u003c/p\u003e\n"],["\u003cp\u003eThese steps help in setting clear objectives and establishing a collaborative framework when working with other ML professionals.\u003c/p\u003e\n"],["\u003cp\u003eApplying ML can raise privacy and ethical issues which need careful consideration before deploying a model, using available resources to mitigate these risks.\u003c/p\u003e\n"],["\u003cp\u003eFurther learning resources are available on data preparation, feature engineering, testing, debugging in ML, and responsible AI practices.\u003c/p\u003e\n"]]],[],null,["# Summary\n\n\u003cbr /\u003e\n\nFraming a problem in terms of ML is a two-step process:\n\n1. Verify that ML is a good approach by doing the following:\n\n - Understand the problem.\n - Identify a clear use case.\n - Understand the data.\n2. Frame the problem in ML terms by doing the following:\n\n - Define the ideal outcome and the model's goal.\n - Identify the model's output.\n - Define success metrics.\n\nThese steps can save time and resources by setting clear goals and providing a\nshared framework for working with other ML practitioners.\n\nUse the following exercises to frame an ML problem and formulate a solution:\n\n- [Framing an ML problem](/machine-learning/problem-framing/try-it/framing-exercise)\n- [Formulating a solution](/machine-learning/problem-framing/try-it/formulate-exercise)\n\nResponsible AI\n--------------\n\nWhen implementing ML solutions, always follow\n[Google's Responsible AI Principles](https://ai.google/responsibility/principles).\n\nFor a hands-on introduction for improving fairness and mitigating bias in\nML, see the [MLCC Fairness module](https://developers.google.com/machine-learning/crash-course/fairness).\n\nKeep learning\n-------------\n\n### More ML learning resources\n\n- [Data Preparation and Feature Engineering](/machine-learning/data-prep)\n- [Testing and Debugging in Machine Learning](/machine-learning/testing-debugging)\n- [People + AI Research](https://pair.withgoogle.com/)"]]
