What is post-selection evaluation in AI?
An example of an AI chatbot conversation about post-selection evaluation in AI systems.
What is post-selection evaluation in AI?
Post-selection evaluation refers to assessing the performance of AI models after they have been trained and selected based on certain criteria.
Why is it important to evaluate AI models after selection?
It's crucial because it helps ensure that the selected model performs well on unseen data and meets the desired objectives.
What methods are commonly used for post-selection evaluation?
Common methods include cross-validation, holdout validation, and performance metrics like accuracy, precision, and recall.
Can you give an example of post-selection evaluation in practice?
Sure! For instance, after selecting a model for image classification, you would test it on a separate dataset to evaluate its accuracy.
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