A hyperparameter is used called “lambda” that controls the weighting of the penalty to the loss function. A default value of 1.0 will give full weightings to the penalty; a value of 0 excludes the penalty. Very small values of lambda, such as 1e-3 or smaller, are common.
A hyperparameter in machine learning is a parameter whose value is utilized to regulate the learning process. Other factors, such as node weights, however, have values that are obtained by training.
Model hyperparameters, which refer to the model selection task and cannot be inferred while fitting the machine to the training set, and algorithm hyperparameters, which in theory have no bearing on the performance of the model but affect the speed and quality of learning, are two categories of hyperparameters. The topology and size of a neural network are two instances of model hyperparameters. Algorithm hyperparameters include things like learning rate, batch size, and mini-batch size.
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