Correct Answer: Option A — A configuration set before training
Explanation:
A hyperparameter is a setting or configuration chosen before training a machine learning model. Unlike model parameters, which are learned f...
A hyperparameter is a setting or configuration chosen before training a machine learning model. Unlike model parameters, which are learned from data during training, hyperparameters are predefined by the user. Examples include the learning rate, batch size, number of epochs, and the number of hidden layers. Proper hyperparameter selection can significantly improve a model's accuracy and performance.