How do I choose validation data for my model?
An example of an AI chatbot conversation about selecting validation data for machine learning models.
How do I choose validation data for my model?
Choosing validation data is crucial for evaluating your model's performance. You should ensure it is representative of the data your model will encounter in the real world.
What factors should I consider when selecting this data?
Consider factors like the distribution of your data, the size of the validation set, and whether it includes edge cases that your model might face.
How much validation data do I need?
A common practice is to use 10-20% of your total dataset for validation, but this can vary based on your specific use case.
Should the validation data be labeled?
Yes, the validation data should be labeled so you can accurately assess your model's performance against known outcomes.
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