Hiperparaméterhúzás

Hiperparaméterhúzás refers to the process of selecting the optimal set of hyperparameters for a machine learning model. Hyperparameters are external configuration settings that are not learned from the data during training, but rather are set before the learning process begins. Examples include learning rate in gradient descent, the number of trees in a random forest, or the regularization strength in a support vector machine.

The goal of hyperparameter tuning is to find a combination of hyperparameter values that results in the best performance for a given model on a specific dataset. This is typically achieved by evaluating the model's performance using different hyperparameter settings on a validation set or through cross-validation. Poorly chosen hyperparameters can lead to underfitting, where the model is too simple to capture the underlying patterns in the data, or overfitting, where the model learns the training data too well, including its noise, and fails to generalize to new, unseen data.

Several strategies exist for hyperparameterhúzás. Grid search systematically explores a predefined set of hyperparameter values. Random search samples hyperparameter values from a specified distribution, which can be more efficient than grid search, especially when some hyperparameters have a larger impact on performance than others. More advanced techniques like Bayesian optimization use probabilistic models to guide the search for optimal hyperparameters, aiming to find good values with fewer trials. The choice of tuning strategy often depends on the complexity of the hyperparameter space and the available computational resources.