In machine learning, hyperparameters and model parameters are two different types of parameters used to define a machine learning model.
Model parameters are the parameters that are learned during the training phase of the machine learning model. They define the relationship between the input data and the output predictions of the model. Examples of model parameters include weights and biases in a neural network, coefficients in a linear regression model, and the clusters in a k-means clustering algorithm. The values of model parameters are adjusted during the training phase to minimize the difference between the actual and predicted outputs.
Hyperparameters, on the other hand, are not learned during the training phase but are set by the user before training the model. Hyperparameters are used to control the behavior of the model during training and affect the performance of the model. Examples of hyperparameters include the learning rate in a neural network, the regularization strength in a linear regression model, and the number of clusters in a k-means clustering algorithm. The choice of hyperparameters can have a significant impact on the performance of the model.
In summary, the main difference between model parameters and hyperparameters is that model parameters are learned during the training phase, while hyperparameters are set before training and affect the behavior of the training algorithm. Choosing appropriate hyperparameters is important for achieving good performance of the machine learning model.