Summary
Framing a problem in terms of ML is a two-step process:
Verify that ML is a good approach by doing the following:
- Understand the problem.
- Identify a clear use case.
- Understand the data.
Frame the problem in ML terms by doing the following:
- Define the ideal outcome and the model's goal.
- Identify the model's output.
- Define success metrics.
These steps can save time and resources by setting clear goals and providing a
shared framework for working with other ML practitioners.
Use the following exercises to frame an ML problem and formulate a solution:
Responsible AI
When implementing ML solutions, always follow
Google's Responsible AI Principles.
For a hands-on introduction for improving fairness and mitigating bias in
ML, see the MLCC Fairness module.
Keep learning
More ML learning resources
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-02-28 UTC.
[null,null,["Last updated 2025-02-28 UTC."],[[["Framing 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."],["These steps help in setting clear objectives and establishing a collaborative framework when working with other ML professionals."],["Applying ML can raise privacy and ethical issues which need careful consideration before deploying a model, using available resources to mitigate these risks."],["Further learning resources are available on data preparation, feature engineering, testing, debugging in ML, and responsible AI practices."]]],[]]