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Found 7 papers, 3 papers with code
Evaluating Probabilistic Classifiers: The Triptych
Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or confidence scores, are ubiquitous in science and society, and methods for evaluating and comparing them are in great demand.
Evaluating probabilistic classifiers: Reliability diagrams and score decompositions revisited
A probability forecast or probabilistic classifier is reliable or calibrated if the predicted probabilities are matched by ex post observed frequencies, as examined visually in reliability diagrams.
Evaluating epidemic forecasts in an interval format
For practical reasons, many forecasts of case, hospitalization and death counts in the context of the current COVID-19 pandemic are issued in the form of central predictive intervals at various levels.
Applications Populations and Evolution
Receiver operating characteristic (ROC) movies, universal ROC (UROC) curves, and coefficient of predictive ability (CPA)
Throughout science and technology, receiver operating characteristic (ROC) curves and associated area under the curve (AUC) measures constitute powerful tools for assessing the predictive abilities of features, markers and tests in binary classification problems.
Isotonic Distributional Regression
Isotonic distributional regression (IDR) is a powerful nonparametric technique for the estimation of conditional distributions under order restrictions.
Methodology Statistics Theory Statistics Theory
Probabilistic Forecasting and Comparative Model Assessment Based on Markov Chain Monte Carlo Output
Based on proper scoring rules, we develop a notion of consistency that allows to assess the adequacy of methods for estimating the stationary distribution underlying the simulation output.
Methodology
Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings
We show that any scoring function that is consistent for a quantile or an expectile functional, respectively, can be represented as a mixture of extremal scoring functions that form a linearly parameterized family.
