Hyperparameters hold a pivotal position in machine-learning algorithms and are indispensable for fine-tuning models to attain optimal performance. Unlike other parameters, hyperparameters are not derived from the data; instead, they are predefined by data scientists or researchers prior to the commencement of the training process.
Hyperparameters are configuration decisions that govern how a machine-learning algorithm extracts knowledge from data. They are user-specified and remain fixed throughout the training process. Common examples of hyperparameters encompass the learning rate, the number of hidden layers in a neural network, the number of decision trees in a random forest, and the regularization parameter in linear regression.