In machine learning, a hyperparameter is a configuration variable that’s external to the model and whose value is not estimated from the data given.
Hyperparameters are an essential part of the process of estimating model parameters and are often defined by the practitioner.
When a machine learning algorithm is used for a specific problem, such as using a grid search or a random search algorithm, then you’re actually tuning the hyperparameters of the model to discover the values that result in the most accurate predictions.
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