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For information retrieval and binary classification, we show that precision at the top (or precision at k) and recall at the top (or recall at k) are maximised by thresholding the posterior probability of the positive class.
Detection, Estimation, and Modulation Theory, Part I
H.L. van Trees · 1968
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Probability and measure
P. Billingsley · 1995
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Probability: Theory and Examples
R. Durrett · 1996
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The foundations of cost-sensitive learning
C. Elkan · 2001
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A Support Vector Method for Multivariate Performance Measures
T. Joachims · 2005
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A Neyman-Pearson approach to statistical learning
C. Scott and R. Nowak · 2005
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Ranking the Best Instances
S. Clémençon and N. Vayatis · 2007
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Introduction to Information Retrieval
C.D. Manning, P. Raghavan, and H. Schütze · 2008
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Accuracy at the Top
S. Boyd, C. Cortes, M. Mohri, and A. Radovanovic · 2012
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A Plug-in Approach to Neyman-Pearson Classification
X. Tong · 2013
Cited alongside, same era.
Consistent Binary Classification with Generalized Performance Metrics
O. Koyejo, N. Natarajan, P. Ravikumar, and I.S. Dhillon · 2014
Cited alongside, same era.
Surrogate functions for maximizing precision at the top
P. Kar, H. Narasimhan, and P. Jain · 2015
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Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration
M. Kull, T.M. Silva Filho, and P. Flach · 2017
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Fisher Consistency for Prior Probability Shift
D. Tasche · 2017
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A Strongly Quasiconvex PAC-Bayesian Bound
N. Thiemann, C. Igel, O. Wintenberger, and Y. Seldin · 2017
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Constrained Classification and Ranking via Quantiles
A. Mackey, X. Luo, and E. Eban · 2018
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