In [[machine learning]], '''hyperparameter optimization'''<ref>Matthias Feurer and Frank Hutter. [https://link.springer.com/content/pdf/10.1007%2F978-3-030-05318-5_1.pdf Hyperparameter optimization]. In: ''AutoML: Methods, Systems, Challenges'', pages 3–38.</ref> or tuning is the problem of choosing a set of optimal [[Hyperparameter (machine learning)|hyperparameters]] for a learning algorithm. A hyperparameter is a [[parameter]] whose value is used to control the learning process.
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 arXiv |eprint=1502.02127|last1=Claesen|first1=Marc|title=Hyperparameter Search in Machine Learning|author2=Bart De Moor|class=cs.LG|year=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, and therefore choose the set of values for hyperparameters that maximize it.<ref name="bergstra">{{cite journal|last1=Bergstra|first1=James|last2=Bengio|first2=Yoshua|year=2012|title=Random Search for Hyper-Parameter Optimization|url=http://jmlr.csail.mit.edu/papers/volume13/bergstra12a/bergstra12a.pdf|journal=Journal of Machine Learning Research|volume=13|pages=281–305}}</ref>
