Hüperparametrite

Hüperparametrite, often spelled hyperparameter, refers to configuration variables that are external to a machine learning model and whose values cannot be estimated from the data. Instead, they are set prior to the learning process. These parameters control the learning algorithm's behavior and influence its performance.

Examples of hyperparameters include the learning rate in gradient descent, the number of hidden layers in a neural network, the regularization parameter, and the kernel type in support vector machines. The choice of hyperparameters can significantly impact the model's ability to generalize to unseen data.

Tuning these parameters is a crucial step in building effective machine learning models. This process, known as hyperparameter optimization or tuning, involves systematically searching for the combination of hyperparameter values that yields the best performance on a validation dataset. Common methods for hyperparameter optimization include grid search, random search, and Bayesian optimization. The goal is to find a set of hyperparameters that balances model complexity and performance, avoiding both underfitting and overfitting.