.png){kind=icon}
返回
Benchmarking in classification and regression
.png){kind=icon}
OA
AI
DOI：10.1002/widm.1318.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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
Keyword:
benchmarking
comparative analysis
data repositories
model validation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11.7
论文数:
544
被引数:
5.3K
机构
引用论文
The use of the area under the roc curve in the evaluation of machine learning algorithmsroc曲线下面积在机器学习算法评价中的应用.png)
PATTERN RECOGNITIONOAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏
查看更多内容