Hyperparameter

Also written: hyperparameters

A setting you choose before training, such as tree depth or learning rate, rather than one the model learns.

Other terms from the same lesson

• cross-validation: Scoring a model several times on different held-back slices of the data, for a steadier estimate.
• fold: One of the equal slices that cross-validation cuts the data into. Each fold takes a turn as the held-back data.
• grid search: Trying every combination from a list of settings, scoring each with cross-validation, and keeping the best.