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Hyperparameter tuning
Hyperparameter tuning is systematically searching over different hyperparameter values to find a combination that performs best on validation data.
Prerequisite: Hyperparameter
What matters
Because hyperparameters interact with each other, tuning them one at a time can miss better combinations; systematic search strategies explore the space more thoroughly than manual guessing.
Example
A tuning process might try 50 different combinations of learning rate and tree depth, keeping whichever combination scores best on the validation set.
Common misconception
Hyperparameter tuning is not guaranteed to keep improving results forever; beyond a certain point, further tuning often yields only marginal or no real gains.
Recall question
Why can tuning hyperparameters one at a time miss better combinations?
Keywords and related concepts
hyperparameter tuninggrid searchmodel optimizationhyperparameter