Parameterafstemning

Parameterafstemning, often translated as parameter tuning or parameter voting, is a method used in machine learning and optimization to select the best set of parameters for a model or algorithm. The core idea is to evaluate a range of parameter values and choose the combination that yields the most desirable outcome. This outcome is typically measured by a performance metric, such as accuracy, precision, recall, or a specific objective function value.

The process generally involves defining a search space for the parameters, which are the configurable settings of an algorithm that are not learned from the data. This search space can be discrete or continuous. A set of candidate parameter combinations is then generated, either systematically or through a more randomized approach. Each candidate combination is used to train and evaluate the model on a validation dataset. The parameter combination that results in the best performance on the validation set is then selected as the optimal set.

Common techniques for parameterafstemning include grid search, where all possible combinations within a defined grid are tested, and random search, where random combinations are sampled from the parameter space. More advanced methods like Bayesian optimization and evolutionary algorithms can also be employed for more efficient exploration of the parameter space, especially when the evaluation of each parameter set is computationally expensive. The goal of parameterafstemning is to improve the generalization performance of a model by avoiding overfitting to the training data.