概览
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。
“问题框架”是分析问题以分离出需要解决的问题单个元素的过程。问题框架有助于确定项目的技术可行性，并提供一套明确的目标和成功标准。在考虑机器学习解决方案时，有效的问题构建可以决定您的产品最终能否成功。
正式问题构建是解决机器学习问题的关键起点，因为它迫使我们更好地了解问题和数据，以便在两者之间设计和构建一座桥梁。- TensorFlow 工程师
概括来讲，机器学习问题构建包括以下两个不同的步骤：
- 确定机器学习是否适合解决问题。
- 使用机器学习术语来限定问题。
检查您的理解情况
为什么问题构图至关重要？
在开始处理数据和训练模型之前，问题框架可确保机器学习方法是有效的解决方案。
问题框架有助于诊断现有机器学习模型存在的问题，并发现数据问题。
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)：2023-10-12。
[null,null,["最后更新时间 (UTC)：2023-10-12。"],[[["\u003cp\u003eProblem framing involves analyzing a problem to identify its core components for effective solutions, determining technical feasibility, and setting clear goals.\u003c/p\u003e\n"],["\u003cp\u003eEffective problem framing is crucial for machine learning projects to succeed, clarifying whether ML is the right approach and framing the problem in ML terms.\u003c/p\u003e\n"],["\u003cp\u003eIt's important because it validates the suitability of an ML approach and aids in diagnosing existing model or data issues before significant resources are invested.\u003c/p\u003e\n"]]],[],null,["# Overview\n\n\u003cbr /\u003e\n\n**Problem framing** is the process of analyzing a problem to isolate the\nindividual elements that need to be addressed to solve it. Problem framing helps\ndetermine your project's technical feasibility and provides a clear set of goals\nand success criteria. When considering an ML solution, effective problem framing\ncan determine whether or not your product ultimately succeeds.\n\u003e Formal problem framing is the critical beginning for solving an ML problem, as it forces us to better understand both the problem and the data in order to design and build a bridge between them. - *TensorFlow engineer*\n\nAt a high level, ML problem framing consists of two distinct steps:\n\n1. Determining whether ML is the right approach for solving a problem.\n2. Framing the problem in ML terms.\n\n### Check Your Understanding\n\nWhy is problem framing important? \nProblem framing ensures that an ML approach is a good solution to the problem before beginning to work with data and train a model. \nProblem framing helps diagnose problems with existing ML models and uncovers issues with data."]]
