.png){kind=icon}
Return
Benchmarking in classification and regression
.png){kind=icon}
OA
AI
DOI：10.1002/widm.1318.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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
Keywords:
benchmarking
comparative analysis
data repositories
model validation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.7
Papers:
546
Citations:
5.3K
Organization
View more organizations
Cited Papers
ShareSave
ShareSaveShareSaveShareSaveShareSaveShareSaveThe use of the area under the roc curve in the evaluation of machine learning algorithms.png)
PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAIShareSave View more