hyperparametere

Hyperparametere (hyperparameters) are configuration values set before training a machine learning model. They are not learned from the data and influence the learning process, model capacity, and convergence. Examples include the learning rate, number of layers, number of units per layer, batch size, regularization strength, dropout rate, optimizer type, and activation functions. They differ from model parameters, such as weights and biases, which the model learns during training.

They can be categorized into architectural hyperparametere (model structure like depth and width), optimization hyperparametere (learning rate, momentum, weight decay), regularization hyperparametere (dropout rate, L1/L2 penalties), and data-related hyperparametere (batch size, data augmentation). Hyperparametere can be continuous, discrete, or categorical.

Selecting hyperparametere is called hyperparameter optimering (HPO). It uses validation performance to assess settings. Common methods include grid search and random search; more efficient approaches include Bayesian optimization (such as Gaussian processes or Tree-structured Parzen Estimator), Hyperband, evolutionary strategies, and gradient-based methods when applicable. A practical approach starts with reasonable defaults and performs staged tuning, gradually refining the search space.

Challenges and best practices: hyperparametere tuning can be computationally expensive and may lead to overfitting on the validation set if not careful. Fixing random seeds, documenting the search strategy, and using reproducible pipelines improve reliability. Reporting the chosen hyperparametere values and the number of configurations evaluated helps reproducibility. Some systems support adaptive or automatic tuning during training, for example learning rate schedules or early stopping, which can reduce tuning overhead.