Benchmarking in classification and regression. (18th June 2019)

• Record Type: Journal Article
• Authors: Hoffmann, Frank; Bertram, Torsten; Mikut, Ralf; Reischl, Markus; Nelles, Oliver
• Is Part Of: Wiley interdisciplinary reviews. Volume 9:Number 5(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-06-18
• Subjects: benchmarking -- comparative analysis -- data repositories -- model validation; Data mining -- Periodicals; 006.31205
• DOI: 10.1002/widm.1318
• Languages: English
• ISSNs: 1942-4787

Abstract

Abstract: The article presents an overview of the status quo in benchmarking in classification and nonlinear regression. It outlines guidelines for a comparative analysis in machine learning, benchmarking principles, accuracy estimation, and model validation. It provides references to established repositories and competitions and discusses the objectives and limitations of benchmarking. Benchmarking is key to progress in machine learning as it allows an unprejudiced comparison among alternative methods. This article presents guidelines and best practices for benchmarking in classification and regression. It reviews state‐of‐the‐art approaches in machine learning, establishes benchmarking principles and discusses performance metrics for a sound statistical comparative analysis. This article is categorized under: Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Machine Learning Technologies > Classification Abstract : Benchmarking, datasets, competitions and statistical analysis.