Hyperparameter optimization: Difference between revisions

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In [[machine learning]], '''Hyperparameterhyperparameter optimization''' or tuning is the problem of choosing a set of optimal [[Hyperparameter (machine learning)|hyperparameters]] for a learning algorithm.

The same kind of machine learning model could require different constraints, weights or learning rates to generalize different data patterns. These measures are called hyperparameters, and have to be tuned so that the model can best solve the machine learning problem. Usually a metric is chosen to measure the algorithm's performance on an independent data set and hyperparameters that maximize this measure are adopted. Often [[Cross-validation (statistics)|cross-validation]] is used to estimate this generalization performance.<ref name="bergstra" />