Predictive Value Generalization Bounds

07/09/2020
by   Keshav Vemuri, et al.
0

In this paper, we study a bi-criterion framework for assessing scoring functions in the context of binary classification. The positive and negative predictive values (ppv and npv, respectively) are conditional probabilities of the true label matching a classifier's predicted label. The usual classification error rate is a linear combination of these probabilities, and therefore, concentration inequalities for the error rate do not yield confidence intervals for the two separate predictive values. We study generalization properties of scoring functions with respect to predictive values by deriving new distribution-free large deviation and uniform convergence bounds. The latter bound is stated in terms of a measure of function class complexity that we call the order coefficient; we relate this combinatorial quantity to the VC-subgraph dimension.

READ FULL TEXT

page 1

page 2

page 3

page 4

06/25/2013

Supersparse Linear Integer Models for Predictive Scoring Systems

We introduce Supersparse Linear Integer Models (SLIM) as a tool to creat...
06/18/2020

Distribution-free binary classification: prediction sets, confidence intervals and calibration

We study three notions of uncertainty quantification—calibration, confid...
10/19/2020

Variance-adaptive confidence sequences by betting

This paper derives confidence intervals (CI) and time-uniform confidence...
08/16/2019

Algorithms and Complexity for Functions on General Domains

Error bounds and complexity bounds in numerical analysis and information...
12/25/2021

Prevalence Threshold and bounds in the Accuracy of Binary Classification Systems

The accuracy of binary classification systems is defined as the proporti...
07/26/2018

Rademacher Generalization Bounds for Classifier Chains

In this paper, we propose a new framework to study the generalization pr...
06/27/2012

Feature Selection via Probabilistic Outputs

This paper investigates two feature-scoring criteria that make use of es...