Hyperparaméternek

Hyperparaméternek is a Hungarian term that translates to "hyperparameter" in English. In the field of machine learning and artificial intelligence, hyperparameters are external configuration variables that are used to control the learning process of a model. Unlike model parameters, which are learned from the data during training, hyperparameters are set before the training begins.

Examples of hyperparameters include the learning rate in gradient descent, the number of hidden layers in a neural network, the regularization strength, the type of activation function, and the kernel type in a support vector machine. The choice of hyperparameters can significantly impact the performance and generalization ability of a machine learning model.

Finding optimal hyperparameters is a crucial step in model development. This process is often referred to as hyperparameter tuning or hyperparameter optimization. Common techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization. Each method explores different combinations of hyperparameter values to identify the set that yields the best performance on a validation dataset. The goal is to strike a balance between underfitting, where the model is too simple to capture the underlying patterns in the data, and overfitting, where the model learns the training data too well, including its noise, and performs poorly on unseen data. Therefore, hyperparaméternek plays a vital role in building effective machine learning systems.