Hyperparameters
nounid
4924·updated Aug 28, 2026verified
the parameters that are used to either configure a ML model (e.g., the penalty parameter C in a support vector machine, and the learning rate to train a neural network) or to specify the algorithm used to minimize the loss function (e.g., the activation function and optimizer types in a neural network, and the kernel type in a support vector machine).
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Data72%llm-generatedllm:claude-haiku-4-5
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Ip65%llm-generatedllm:claude-haiku-4-5
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Framework definitions
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice1 senseview framework →
- §1
- the parameters that are used to either configure a ML model (e.g., the penalty parameter C in a support vector machine, and the learning rate to train a neural network) or to specify the algorithm used to minimize the loss function (e.g., the activation function and optimizer types in a neural network, and the kernel type in a support vector machine).
- §1 · legacy_primary
- the parameters that are used to either configure a ML model (e.g., the penalty parameter C in a support vector machine, and the learning rate to train a neural network) or to specify the algorithm used to minimize the loss function (e.g., the activation function and optimizer types in a neural network, and the kernel type in a support vector machine).DR-088 backfill from the noun definition column
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