A model parameter is a configuration variable that is internal to the model and whose value can be estimated from data, Whereas a model hyperparameter is a configuration that is external to the model and whose value cannot be estimated from data.
Some examples of model parameters include:
- The weights in an artificial neural network.
- The support vectors in a support vector machine.
- The coefficients in a linear regression or logistic regression.
Some examples of model hyperparameters include:
- The learning rate for training a neural network.
- The C and sigma hyperparameters for support vector machines.
- The k in k-nearest neighbors
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