About: Hyperparameter (machine learning)

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In machine learning, 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 derived via training. Hyperparameters can be classified as model hyperparameters, that cannot be inferred while fitting the machine to the training set because they refer to the model selection task, or algorithm hyperparameters, that in principle have no influence on the performance of the model but affect the speed and quality of the learning process. An example of a model hyperparameter is the topology and size of a neural network. Examples of algorithm hyperparameters are learning rate and mini-.

Attributes	Values
rdf:type	Thing
rdfs:label	Hyperparameter (machine learning) (en)
rdfs:comment	In machine learning, 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 derived via training. Hyperparameters can be classified as model hyperparameters, that cannot be inferred while fitting the machine to the training set because they refer to the model selection task, or algorithm hyperparameters, that in principle have no influence on the performance of the model but affect the speed and quality of the learning process. An example of a model hyperparameter is the topology and size of a neural network. Examples of algorithm hyperparameters are learning rate and mini-. (en)
differentFrom	Hyperparameter
dbp:wikiPageUsesTemplate	dbt:Clarify dbt:Distinguish dbt:Main dbt:Reflist dbt:Differentiable_computing
Subject	Machine learning Model selection
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)?oldid=1072258263&ns=0
Wikipage page ID	32402755 (xsd:integer)
page length (characters) of wiki page	12021 (xsd:nonNegativeInteger)
Wikipage revision ID	1072258263 (xsd:integer)
Link from a Wikipage to another Wikipage	Machine learning Machine learning Model selection PostgreSQL Curve fitting Deep learning Hyper-heuristic Robustness (computer science) Ordinary least squares Parameter Model selection MongoDB Training, validation, and test sets Long short-term memory Loss function Lasso (statistics) Learning rate Random seed Regularization (mathematics) Reinforcement learning Replication crisis Reproducibility dbr:Batch_size
has abstract	In machine learning, 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 derived via training. Hyperparameters can be classified as model hyperparameters, that cannot be inferred while fitting the machine to the training set because they refer to the model selection task, or algorithm hyperparameters, that in principle have no influence on the performance of the model but affect the speed and quality of the learning process. An example of a model hyperparameter is the topology and size of a neural network. Examples of algorithm hyperparameters are learning rate and mini-. Different model training algorithms require different hyperparameters, some simple algorithms (such as ordinary least squares regression) require none. Given these hyperparameters, the training algorithm learns the parameters from the data. For instance, LASSO is an algorithm that adds a regularization hyperparameter to ordinary least squares regression, which has to be set before estimating the parameters through the training algorithm. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)
is Wikipage redirect of	Grid search method Hyper-parameter Optimization Tuning hyperparameters
is Link from a Wikipage to another Wikipage of	AlphaGo Zero AlphaZero Apache MXNet AutoAI Cross-validation (statistics) Deep Learning Studio Hyperparameter optimization Convolutional neural network Gradient boosting Learning rate MuZero Grid search method Hyper-parameter Optimization
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)

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