Hyperparameterscontrollable

Hyperparameterscontrollable refers to a set of parameters in a machine learning model that are not learned from the data but are set before the training process begins. These values significantly influence the model's behavior and performance. Unlike model parameters, which are adjusted by the learning algorithm during training to minimize error, hyperparameters are external configurations.

Examples of common hyperparameters include the learning rate in gradient descent, the number of layers and neurons in a neural network, the regularization strength, and the choice of activation function. The selection of appropriate hyperparameters is crucial for achieving optimal results. If hyperparameters are not chosen wisely, the model may underfit (fail to capture the underlying patterns in the data) or overfit (learn the training data too well, leading to poor generalization on unseen data).

The process of finding the best set of hyperparameters is known as hyperparameter tuning or optimization. Various techniques are employed for this purpose, such as grid search, random search, Bayesian optimization, and evolutionary algorithms. Each method systematically explores a range of hyperparameter values to identify the combination that yields the best performance on a validation set. The goal of hyperparameter tuning is to improve the model's accuracy, efficiency, and robustness.