reguleringsparameter
Reguleringsparameter is a term used in statistical modeling and machine learning to refer to a parameter that controls the complexity of a model, often by adding a penalty term to the loss function during training. This penalty discourages overly complex models, which can lead to overfitting, a phenomenon where a model performs well on training data but poorly on unseen data.
The primary goal of a reguleringsparameter is to improve the generalization ability of a model. By introducing
Common examples of regularization techniques that utilize reguleringsparameters include L1 regularization (Lasso) and L2 regularization (Ridge).
The optimal value of a reguleringsparameter is typically found through hyperparameter tuning, often using techniques like