Hyperparameter

A setting chosen before training, not learned from the data.

A configuration value (the regularization strength $\lambda$ , the number of neighbors $k$ , a tree's maximum depth) fixed before the fitting procedure runs, typically chosen via cross-validation rather than solved for the way a parameter is.

Examples

Ridge regression's $\lambda$ is a hyperparameter; the coefficients it produces are parameters.