01 · Understand the idea
In simple words
When preparing for an exam, you study examples, use practice tests to improve and finally take an unseen exam. ML evaluation uses a similar separation. Training fits the model, validation guides your choices and testing checks the final design on held-out examples. The separation helps make your score more honest.
Two useful terms
Training set: examples used to fit a model. Held-out data: examples kept separate from fitting.
Build your understanding
A model needs evaluation on data it did not learn from. Training data fits parameters, validation data supports model choices and test data provides a final held-out check. The split must reflect how the system will be used: time, users and groups can create dependencies between rows.
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