Hyperparametre

Hyperparameters are a type of parameter used in machine learning and statistical modeling that are not learned from the data but instead set prior to the training process. They are essential in determining the behavior and performance of a model. Unlike model parameters, which are learned during training, hyperparameters are set by the practitioner or through automated methods like grid search or random search.

Hyperparameters can influence various aspects of a model, including its complexity, capacity, and generalization ability. Examples of hyperparameters include the learning rate in gradient descent algorithms, the number of trees in a random forest, the number of hidden layers and neurons in a neural network, and the regularization parameters in models like ridge or lasso regression.

The choice of hyperparameters can significantly impact the model's performance. Setting them appropriately is crucial for achieving good results. However, finding the optimal hyperparameters can be challenging, as it often involves a trade-off between model complexity and performance. Techniques such as cross-validation, Bayesian optimization, and evolutionary algorithms are commonly used to search for optimal hyperparameters.

In summary, hyperparameters play a vital role in machine learning and statistical modeling. They are set before the training process and can greatly influence the model's performance. Selecting the right hyperparameters is essential for building effective and efficient models.