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Parameters VS Hyperparameters ( Parameter VS Hyperparameter ) in Machine Learning

Parameters in a Machine Learning model are the parameters whose values are updated during training using some optimization procedure.

Hyperparameters in a Machine Learning model are the parameters whose values are decided before training of the model begins. Hyperparameters are not affected (do not change) by training, rather, these affect the quality and speed of the training. So in a sense, these parameters are over, above or beyond ( hyper- ) the process of training and hence called Hyperparameters.

Hyperparameters can be related to model selection, e.g., model type, model architecture, or learning algorithm, e.g., learning rate, batch size, number of epochs.

There are no efficient algorithms to select optimal (best) values of hyperparameters. So optimal values of hyperparameters are determined using a trial and error process by adjusting or adapting their values for a particular machine learning task. This process of determining values of hyperparameters is called Hyperparameter Tuning. For example select the hyperparameters that give best results on validation set.

What is the difference between parameters and hyperparameters in machine learning ?
What is the difference between parameter and hyperparameter in machine learning ?
What is hyperparameter in machine learning ?
What is hyperparameter ?
Why hyperparameters are called hyperparameters ?
Why hyperparameters are called so ?
What is hyperparameter tuning ?
How do we find values of hyperparameters ?
[Description Credits : Pankaj Porwal]

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