hiperparaméterhúzással

Hiperparaméterhúzás refers to the process of selecting optimal hyperparameters for machine learning models. Hyperparameters are settings that are not learned from the data but are set before the training process begins. Examples include the learning rate in gradient descent, the number of layers in a neural network, or the C parameter in a Support Vector Machine. The choice of hyperparameters can significantly impact a model's performance.

The goal of hiperparaméterhúzás is to find a combination of hyperparameters that results in the best possible performance on unseen data, often measured by metrics like accuracy, precision, or recall. This is typically achieved by training and evaluating the model multiple times with different hyperparameter configurations.

Common methods for hiperparaméterhúzás include grid search, random search, and Bayesian optimization. Grid search exhaustively tries all possible combinations of hyperparameters specified within a given range. Random search, on the other hand, samples hyperparameter combinations randomly from a defined distribution, which can be more efficient for high-dimensional hyperparameter spaces. Bayesian optimization uses probabilistic models to intelligently select the next set of hyperparameters to evaluate, aiming to converge faster to the optimal settings by leveraging information from previous evaluations. Automated machine learning (AutoML) platforms often incorporate sophisticated hiperparaméterhúzás techniques to streamline the model development process.