Hyperparametriyhdistelmä

Hyperparametriyhdistelmä refers to a specific set of values for the hyperparameters of a machine learning model. Machine learning models have parameters that are learned from data during training, such as the weights in a neural network. In contrast, hyperparameters are external configuration settings that are not learned from the data. Examples of hyperparameters include the learning rate in an optimization algorithm, the number of hidden layers in a neural network, or the regularization strength.

The performance of a machine learning model is highly dependent on the choice of its hyperparameters. Finding the optimal set of hyperparameters is crucial for achieving the best possible results. The process of searching for the best hyperparameter combination is known as hyperparameter tuning or optimization.

A hyperparametriyhdistelmä is one particular configuration of these settings that is evaluated during the tuning process. For instance, if a model has two hyperparameters, learning rate and batch size, a specific hyperparametriyhdistelmä could be (learning_rate=0.01, batch_size=32). Different hyperparametriyhdistelmät are typically tested using techniques like grid search, random search, or Bayesian optimization. The hyperparametriyhdistelmä that yields the best performance on a validation dataset is then selected as the final configuration for the model. This systematic exploration of different hyperparameter combinations aims to prevent overfitting and improve the model's generalization ability to unseen data.