Hyperparameters are configuration values set before or during training that control how a machine learning algorithm learns. Examples include learning rate, number of trees, batch size, regularization strength, or model architecture choices. Unlike model parameters such as weights, hyperparameters are not learned directly from training examples.
A parameter of a machine learning algorithm. Examples include the number of trees to learn in a decision forest or the step size in a gradient descent algorithm. Values of Hyperparameters are set before training the model and govern the process of finding the parameters of the prediction function, for example, the comparison points in a decision tree or the weights in a linear regression model. For more information, see the Hyperparameter article on Wikipedia.
A parameter that can be set in order to define any configurable part of a machine learning model's learning process.
The variables that you or a hyperparameter tuning service adjust during successive runs of training a model. For example, learning rate is a hyperparameter. You could set the learning rate to 0.01 before one training session. If you determine that 0.01 is too high, you could perhaps set the learning rate to 0.003 for the next training session. In contrast, parameters are the various weights and bias that the model learns during training. See Linear regression: Hyperparameters in Machine Learning Crash Course for more information.