Hiperparaméterhúzás
Hiperparaméterhúzás refers to the process of selecting the optimal set of hyperparameters for a machine learning model. Hyperparameters are external configuration settings that are not learned from the data during training, but rather are set before the learning process begins. Examples include learning rate in gradient descent, the number of trees in a random forest, or the regularization strength in a support vector machine.
The goal of hyperparameter tuning is to find a combination of hyperparameter values that results in the
Several strategies exist for hyperparameterhúzás. Grid search systematically explores a predefined set of hyperparameter values. Random