Machine Learning [1]#

Hyperparameters is a parameter whose value is set before the learning process begins. By contrast, the values of other parameters are derived via training.

Different Machine Learning model's training Machine Learning Algorithms require different Hyperparameters. Given these hyperparameters, the training algorithm learns the parameters from the data. For instance, LASSO is an algorithm that adds a regularization hyperparameter to OLS regression, which has to be set before estimating the parameters through the training algorithm.

Some simple algorithms (such as ordinary least squares regression) require none.

Hyperparameters Statistics [2]#

Bayesian Hyperparameters is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis.

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