hiperparaméterkombinációkat

Hyperparameters are external configuration variables that are set before the training process of a machine learning model begins. Unlike model parameters, which are learned from the data during training, hyperparameters are chosen by the practitioner. They control various aspects of the learning process, such as the complexity of the model, the speed of convergence, and the generalization ability.

Examples of common hyperparameters include the learning rate in gradient descent, which determines the step size taken during parameter updates; the number of hidden layers and the number of neurons per layer in neural networks, which define the model's capacity; and regularization parameters like L1 or L2 penalties, which help prevent overfitting. The choice of hyperparameters can significantly impact the performance of a model.

Finding the optimal set of hyperparameters is a crucial step in model development. This process is often referred to as hyperparameter tuning or optimization. Common techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization. Grid search exhaustively searches a predefined set of hyperparameter values. Random search samples hyperparameter values randomly from a specified distribution. Bayesian optimization uses probabilistic models to intelligently select the next set of hyperparameters to evaluate, aiming to find the optimum more efficiently. The goal of hyperparameter tuning is to find a combination that results in the best performance on unseen data, typically evaluated using a validation set.