hyperparaméteroptimalizálását

Hyperparameters are external configuration variables used to control the learning process of a machine learning model. Unlike model parameters, which are learned from the training data, hyperparameters are set before training begins and remain fixed during the training phase. The choice of hyperparameters significantly influences a model's performance, affecting its ability to generalize to unseen data.

Common hyperparameters include the learning rate, which determines the step size during gradient descent optimization; the number of hidden layers and neurons in a neural network; the regularization strength, used to prevent overfitting; and parameters specific to certain algorithms, such as the kernel type and regularization parameter C in Support Vector Machines, or the number of trees and maximum depth in Random Forests.

The process of selecting optimal hyperparameters is known as hyperparameter tuning or hyperparameter optimization. This often involves systematically searching through a range of possible values for each hyperparameter to find the combination that yields the best performance on a validation dataset. Popular hyperparameter tuning techniques include grid search, random search, and more advanced methods like Bayesian optimization. Finding the right hyperparameters is crucial for achieving satisfactory results and effectively deploying machine learning models.