A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification

A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification

This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with ca...

Full description

Saved in:
Bibliographic Details
Main Authors: Umberto Michelucci, Michela Sperti, Dario Piga, Francesca Venturini, Marco A. Deriu
Format: article
Language:EN
Published: MDPI AG 2021
Subjects:
machine learning
intrinsic limits
ROC curve
binary classification
area under the curve
Naïve Bayes classifier
Industrial engineering. Management engineering
T55.4-60.8
Electronic computers. Computer science
QA75.5-76.95
Online Access:https://doaj.org/article/add5c84becaa4f53811e8bb6d2edb647
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doaj.org/article/add5c84becaa4f53811e8bb6d2edb647

Similar Items