HyperparameterOptimierungstechniken

Hyperparameters are configuration settings used to control the training of machine learning models. They are not learned from the data during training; instead, they are set before learning begins and determine how the algorithm searches the parameter space and how quickly it converges.

Examples include learning rate, batch size, number of layers and units, activation functions, regularization strength, dropout rate, momentum, optimizer choice, and weight initialization. Some hyperparameters are architectural (how the model is structured) while others govern the optimization process (how the model is trained).

Hyperparameter tuning is the process of selecting values that maximize generalization performance on a validation set. Common methods are manual tuning, grid search, random search, Bayesian optimization, Hyperband, and other multi-fidelity approaches. Computational cost often dominates; thus efficient search strategies and early stopping are commonly used.

Good practices include starting from sensible defaults, using smaller experiments to narrow the search, and reporting the final hyperparameter configuration along with performance. Hyperparameter choices can significantly affect model quality and training time, and they may interact with data characteristics and hardware constraints.