Hiperparaméterértékeket

Hiperparaméterértékeket are configuration settings that control the learning process of a machine learning model. Unlike model parameters, which are learned from the data during training, hyperparameters are set before training begins. They influence how the model learns, its complexity, and its generalization ability.

Common examples of hyperparameters include the learning rate in gradient descent algorithms, the number of layers and neurons in a neural network, the regularization strength (e.g., L1 or L2 penalties), the choice of activation function, and the kernel type and parameters in Support Vector Machines. The performance of a model is highly dependent on the careful selection of these values.

The process of finding the optimal set of hyperparameters is known as hyperparameter tuning or optimization. This often involves experimenting with different combinations of hyperparameter values and evaluating the model's performance on a validation set. Popular hyperparameter tuning techniques include grid search, random search, and Bayesian optimization. Each method systematically explores the hyperparameter space to identify values that lead to the best predictive accuracy and generalization. Incorrectly chosen hyperparameters can lead to overfitting (the model performs well on training data but poorly on unseen data) or underfitting (the model is too simple to capture the underlying patterns in the data). Therefore, effective hyperparameter tuning is a crucial step in building robust and accurate machine learning models.