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PARAMETER vs HYPERPARAMETER in AI

Model parameters are estimated from data automatically and model hyperparameters are set manually and are used in processes to help estimate model parameters.
PARAMETER vs HYPERPARAMETER in AI

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.

Source- https://www.hitechnectar.com/wp-content/uploads/2020/04/Hyperparameter-vs.-Parameter-Differences-Tabular-Diagram.jpg

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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What is the Difference Between a Parameter and a Hyperparameter?
It can be confusing when you get started in applied machine learning. There are so many terms to use and […]
Source- https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/

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