Hyperparameter

Hyperparameters are configurations that are external to the model and whose values cannot be estimated from the data. They are crucial in determining the performance of machine learning algorithms, as they influence the training process and model architecture. Examples include learning rate, batch size, and the number of hidden layers in a neural network. Tuning these hyperparameters can significantly improve model accuracy and effectiveness.