For linear regression what are the Hyperparameters?1 pointBach size Slope ,Bias Learning rate

In linear regression, the hyperparameters typically include:

1. Learning Rate: This is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. It decides how quickly the model is adapted to the problem.

2. Regularization Parameters: Regularization is a technique used to prevent overfitting by adding an additional penalty term to the loss function. The amount of regularization to apply is controlled by a hyperparameter. In linear regression, this could be the L1 or L2 regularization parameter.

Please note that the slope and bias are not hyperparameters, but parameters of the model. They are learned from the data, not set in advance. Similarly, batch size is typically considered a parameter of the training process, not a hyperparameter of the model itself.