Difference between Parameters and Hyperparameters in Machine Learning

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If we take an example of a linear regression, then the parameters of the model will be the weights and the bias of the model.

And the hyperparameters of the linear regression is the learning rate, the number of epochs, the degree of the polynomial if we try to fit a polynomial regression.

Rule of thumb –

Hyperparameter – Any quantity that you set before the training process is a hyperparameter.

Parameter – Any quantity that the model creates or modifies during the training process is a parameter.

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