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The same kind of machine learning model can 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 optimally solve the machine learning problem. Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal model which minimizes a predefined [[loss function]] on given independent data.<ref name=abs1502.02127>{{cite article |url=https://arxiv.org/abs/1502.02127 |title=Claesen, Marc, and Bart De Moor. "Hyperparameter Search in Machine Learning." arXiv preprint arXiv:1502.02127 (2015).}}</ref> The objective function takes a tuple of hyperparameters and returns the associated loss.<ref name=abs1502.02127/> [[Cross-validation (statistics)|Cross-validation]] is often used to estimate this generalization performance.<ref name="bergstra" />
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=== Grid search ===
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