hiperoparamétereket

Hiperparaméterek are configuration variables that are external to the model and whose values cannot be estimated from data. They are used to control the learning process of machine learning algorithms. Unlike model parameters, which are learned from the training data, hyperparameters must be set before the training begins. Examples of hyperparameters include the learning rate in gradient descent, the number of hidden layers in a neural network, or the regularization strength.

The process of finding the optimal set of hyperparameters for a given problem is called hyperparameter tuning or hyperparameter optimization. This is a crucial step in building effective machine learning models. Common methods for hyperparameter tuning include grid search, random search, and Bayesian optimization. Grid search involves exhaustively searching over a manually specified subset of the hyperparameter space. Random search samples a fixed number of values from probability distributions for each hyperparameter. Bayesian optimization uses probabilistic models to guide the search for optimal hyperparameters, aiming to find better values more efficiently.

The choice of hyperparameters can significantly impact the performance of a machine learning model. Poorly chosen hyperparameters can lead to underfitting or overfitting, where the model either fails to capture the underlying patterns in the data or learns the training data too well, including its noise, leading to poor generalization on unseen data. Therefore, careful consideration and systematic tuning of hyperparameters are essential for achieving good predictive accuracy and robust model performance.