Venn-Abers Predictors

This paper continues study, both theoretical and empirical, of the method of Venn prediction, concentrating on binary prediction problems. Venn predictors produce probability-type predictions for the labels of test objects which are guaranteed to be well calibrated under the standard assumption that the observations are generated independently from the same distribution. We give a simple formalization and proof of this property. We also introduce Venn-Abers predictors, a new class of Venn predictors based on the idea of isotonic regression, and report promising empirical results both for Venn-Abers predictors and for their more computationally efficient simplified version.

[1]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[2]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[3]  John Langford,et al.  Estimating Class Membership Probabilities using Classifier Learners , 2005, AISTATS.

[4]  H. D. Brunk,et al.  AN EMPIRICAL DISTRIBUTION FUNCTION FOR SAMPLING WITH INCOMPLETE INFORMATION , 1955 .

[5]  M. Kendall Theoretical Statistics , 1956, Nature.

[6]  Rich Caruana,et al.  Obtaining Calibrated Probabilities from Boosting , 2005, UAI.

[7]  Xiaoqian Jiang,et al.  Smooth Isotonic Regression: A New Method to Calibrate Predictive Models , 2011, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[8]  Stephen E. Fienberg,et al.  Testing Statistical Hypotheses , 2005 .

[9]  W. Gasarch,et al.  The Book Review Column 1 Coverage Untyped Systems Simple Types Recursive Types Higher-order Systems General Impression 3 Organization, and Contents of the Book , 2022 .

[10]  P. Bartlett,et al.  Probabilities for SV Machines , 2000 .

[11]  H. D. Brunk,et al.  Statistical inference under order restrictions : the theory and application of isotonic regression , 1973 .

[12]  Bianca Zadrozny,et al.  Transforming classifier scores into accurate multiclass probability estimates , 2002, KDD.

[13]  A. H. Murphy,et al.  Verification of Probabilistic Predictions: A Brief Review , 1967 .

[14]  Vladimir Vovk,et al.  Self-calibrating Probability Forecasting , 2003, NIPS.

[15]  Harris Papadopoulos,et al.  Reliable Probability Estimates Based on Support Vector Machines for Large Multiclass Datasets , 2012, AIAI.