About: Hyperparameter optimization

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In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned.

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rdf:type	disease
rdfs:label	Hyperparameter optimization (en)
rdfs:comment	In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. (en)
sameAs	Hyperparameter optimization
dbp:wikiPageUsesTemplate	dbt:Main dbt:Reflist dbt:Differentiable_computing
Subject	Machine learning Mathematical optimization Model selection
thumbnail	wiki-commons:Special:FilePath/Hyperparameter_Optimization_using_Grid_Search.svg?width=300
foaf:depiction
gold:hypernym	Problem
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Hyperparameter_optimization?oldid=1068833517&ns=0
Wikipage page ID	54361643 (xsd:integer)
page length (characters) of wiki page	31203 (xsd:nonNegativeInteger)
Wikipage revision ID	1068833517 (xsd:integer)
Link from a Wikipage to another Wikipage	Machine learning Support-vector machine XGBoost Apache Spark Automated machine learning Automatic differentiation Bayesian optimization Radial basis function Radial basis function kernel Self-tuning Semidefinite programming Statistical classification Brute-force search CMA-ES Caret Cartesian product Genetic programming Machine learning Mathematical optimization Model selection Differential evolution Dlib Meta-optimization Mutation (genetic algorithm) Cross-validation (statistics) Crossover (genetic algorithm) Curse of dimensionality Deep learning Hyperparameter (machine learning) Trust region Parameter Particle swarm optimization Spectral method Embarrassingly parallel Fitness function Keras Kernel density estimation Scikit-learn Evolution Evolution strategy Evolutionary algorithm Model selection Loss function Neural Network Intelligence Neural architecture search R (programming language)
has abstract	In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. 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. The objective function takes a tuple of hyperparameters and returns the associated loss. Cross-validation is often used to estimate this generalization performance. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Hyperparameter_optimization
is Wikipage redirect of	Grid search Hyperparameter tuning
is Link from a Wikipage to another Wikipage of	Automated machine learning Bias–variance tradeoff Genetic algorithm Dimensionality reduction Deep learning K-nearest neighbors algorithm Line search List of numerical analysis topics Convolutional neural network List of algorithms Elie Bursztein Embarrassingly parallel Fairness (machine learning) Feature selection Manifold regularization Model selection Learning rate Multidisciplinary design optimization Multitask optimization Neural Network Intelligence Neural architecture search Grid search Hyperparameter tuning
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/Hyperparameter_optimization

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