hyperparameterhúzás

Hyperparameterhúzás, often translated as hyperparameter search or hyperparameter optimization, refers to the process of finding the optimal set of hyperparameters for a machine learning model. Hyperparameters are parameters whose values are set before the training process begins and are not learned from the data. Examples include the learning rate in gradient descent, the number of trees in a random forest, or the regularization strength in a neural network.

The goal of hyperparameterhúzás is to improve the performance of a machine learning model by systematically exploring different combinations of hyperparameter values and selecting the combination that yields the best results on a validation dataset. This process is crucial because the choice of hyperparameters can significantly impact a model's accuracy, generalization ability, and training time.

Common methods for hyperparameterhúzás include grid search, random search, and Bayesian optimization. Grid search exhaustively searches a predefined grid of hyperparameter values. Random search samples hyperparameters randomly from specified distributions. Bayesian optimization uses probabilistic models to guide the search, making it more efficient by intelligently selecting promising hyperparameter combinations. Evaluating each hyperparameter configuration typically involves training the model with those settings and measuring its performance using metrics like accuracy, F1-score, or mean squared error on a separate validation set. The set of hyperparameters that results in the best performance is then chosen for the final model.