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.

中文翻译：

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分类和回归基准

本文概述了分类和非线性回归中基准测试的现状。它概述了机器学习中的比较分析，基准原则，准确性估计和模型验证的指南。它为已建立的存储库和竞争提供了参考，并讨论了基准测试的目标和局限性。基准测试是机器学习取得进展的关键，因为它可以在不同方法之间进行无偏见的比较。本文介绍了分类和回归基准测试的指南和最佳实践。它回顾了机器学习的最新方法，确立了基准测试原则，并讨论了性能指标，以进行合理的统计比较分析。