VorhersageScores

VorhersageScores are a class of quantitative metrics designed to assess the quality of forecasts generated by predictive models. They provide a compact summary of how close predictions are to observed outcomes and how well forecast probabilities reflect real frequencies. The term can apply to probabilistic forecasts, point forecasts with uncertainty, or ensemble predictions.

Structure and interpretation: Scores may be scalar values, standardized to a convenient range, or multi-dimensional profiles reporting components such as accuracy, calibration, and discrimination. Common components include the Brier score, which measures probabilistic accuracy and calibration; and the logarithmic loss (log loss). Discrimination is often evaluated with metrics like the area under the ROC curve (AUC). Some VorhersageScores combine elements into a single composite score, while others report them separately.

Calculation: For a set of forecasts p_t and binary outcomes y_t, the Brier score is the mean of (p_t − y_t)^2, while log loss is the negative average of y_t log p_t and (1 − y_t) log(1 − p_t). Calibration can be examined with reliability diagrams and calibration curves; discrimination with AUC or rank-based measures. A VorhersageScore may weight components or present them independently to reflect different forecasting goals.

Applications: VorhersageScores are used across meteorology, finance, epidemiology, energy demand forecasting, and sports analytics to compare models, tune prediction systems, and communicate forecast quality to stakeholders.

Limitations: Scores depend on chosen components and baselines; they can be sensitive to class imbalance and to the distribution of outcomes. Composite scores may obscure weaknesses in individual aspects, so multi-metric reporting is common. Proper use requires transparent methodology and validation on independent data.

See also: Brier score, log loss, calibration, discrimination, proper scoring rules, probabilistic forecasting.