Valideringskurvor

Valideringskurvor are graphical representations used to assess and validate the performance of statistical or machine learning models, as well as the validity of measurement methods. In machine learning, a valideringskurva typically shows model performance, such as accuracy or error, as a function of a hyperparameter (for example, regularization strength, tree depth, or number of estimators), often alongside a training- or cross-validation curve. They help identify overfitting and underfitting and guide hyperparameter tuning and model selection. Learning curves, which plot performance versus training set size, are closely related but conceptually distinct.

In measurement science and laboratory validation, valideringskurvor may refer to calibration curves that relate instrument output to known standards. These curves assess linearity, accuracy, and limits of detection, and are used to validate that the measurement method meets predefined criteria.

Methodology: select a metric, perform systematic variation of the parameter or conditions, compute performance, plot curves, and interpret differences between curves. Consider confidence intervals or repeated measurements to gauge uncertainty. Use the curves to compare models or methods and to document validation evidence.

Limitations: curves depend on data quality and the range of parameters tested; extrapolation beyond tested ranges is unreliable; curves should be complemented by additional validation experiments and statistical tests.

See also: calibration curve, learning curve, cross-validation, model validation, overfitting, underfitting.