Hyperparameters

nounverified·updated Aug 28, 2026

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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).

Framework senses

2 senses across 2 frameworks

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).

Legacy lexicon import1 senseview framework →

§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).
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