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• W2951753889 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" @default.
• W2951753889 created "2019-06-27" @default.
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• W2951753889 date "2019-06-18" @default.
• W2951753889 modified "2023-10-18" @default.
• W2951753889 title "Benchmarking in classification and regression" @default.
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• W2951753889 doi "https://doi.org/10.1002/widm.1318" @default.
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