Hiperparaméterre

Hiperparaméterre is a Hungarian term that translates to "hyperparameter" in English. In the context of machine learning, hyperparameters are external configuration variables that are set before the training process begins. They are not learned from the data itself, unlike model parameters. Instead, they control aspects of the learning algorithm's behavior.

Examples of hyperparameters include the learning rate in gradient descent, the number of hidden layers and neurons in a neural network, the regularization strength, the choice of activation function, or the kernel type in support vector machines. The selection of appropriate hyperparameters is crucial for achieving optimal performance of a machine learning model. Poorly chosen hyperparameters can lead to underfitting (the model is too simple to capture the underlying patterns) or overfitting (the model learns the training data too well, including noise, and generalizes poorly to new data).

The process of finding the best set of hyperparameters for a given task is known as hyperparameter tuning or hyperparameter optimization. Common techniques for hyperparameter tuning include grid search, random search, Bayesian optimization, and evolutionary algorithms. These methods systematically explore different combinations of hyperparameter values to identify the configuration that yields the best performance on a validation set or through cross-validation. The goal is to find hyperparameters that allow the model to generalize well to unseen data.