hyperparaméter

Hyperparameters are settings or configurations that are used to control the learning process of a machine learning model. Unlike model parameters, which are learned from the data, hyperparameters are set before the training process begins and are not updated during training. They play a crucial role in determining the performance and behavior of the model.

Common examples of hyperparameters include the learning rate, which controls the step size during gradient descent; the number of layers and neurons in a neural network; and the regularization parameter, which helps prevent overfitting. The choice of hyperparameters can significantly impact the model's ability to generalize to new, unseen data.

Hyperparameter tuning is the process of selecting the optimal set of hyperparameters for a given model and dataset. This can be done manually, through grid search, random search, or more advanced techniques like Bayesian optimization and hyperband. The goal is to find a balance that maximizes the model's performance while avoiding overfitting or underfitting.

In summary, hyperparameters are essential components in the design and training of machine learning models. Their careful selection and tuning are vital for achieving optimal model performance.