验证机器学习岭回归模型使用蒙特卡罗,bootstrap和交叉验证的变化

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2023-01-01 DOI:10.1515/jisys-2022-0224
Robbie T. Nakatsu
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引用次数: 0

摘要

近年来,机器学习从业者多次呼吁提供更多关于如何使用机器学习方法和技术的指导方针。例如,目前关于重采样方法的文献是混乱的,有时甚至是矛盾的;更糟糕的是,有时根本没有提供实用的指导方针。为了解决这一缺点,进行了一项模拟研究,评估了在五个真实数据集上拟合的脊回归模型。研究比较了蒙特卡罗重采样、自举、k-fold交叉验证和重复k-fold交叉验证四种重采样方法的性能。目标是通过使用这些重采样方法的九种变体,找到将均方误差最小化的最佳拟合λ(正则化)参数。对于9个重新采样变量中的每一个,执行1,000次运行,以查看选择良好拟合、平均拟合和差拟合λ值的频率。选择拟合值次数最多的重采样方法为最佳方法。根据调查结果,提出了三个一般性建议:(1)重复k-fold交叉验证是通用重采样方法的最佳选择;(2)在k-fold交叉验证中,k = 10是较好的选择;(3)蒙特卡罗和bootstrap表现不佳,因此不推荐它们作为通用重采样方法。同时,没有一种重采样方法的均匀性优于其他方法。
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Validation of machine learning ridge regression models using Monte Carlo, bootstrap, and variations in cross-validation
Abstract In recent years, there have been several calls by practitioners of machine learning to provide more guidelines on how to use its methods and techniques. For example, the current literature on resampling methods is confusing and sometimes contradictory; worse, there are sometimes no practical guidelines offered at all. To address this shortcoming, a simulation study was conducted that evaluated ridge regression models fitted on five real-world datasets. The study compared the performance of four resampling methods, namely, Monte Carlo resampling, bootstrap, k-fold cross-validation, and repeated k-fold cross-validation. The goal was to find the best-fitting λ (regularization) parameter that would minimize mean squared error, by using nine variations of these resampling methods. For each of the nine resampling variations, 1,000 runs were performed to see how often a good fit, average fit, and poor fit λ value would be chosen. The resampling method that chose good fit values the greatest number of times was deemed the best method. Based on the results of the investigation, three general recommendations are made: (1) repeated k-fold cross-validation is the best method to select as a general-purpose resampling method; (2) k = 10 folds is a good choice in k-fold cross-validation; (3) Monte Carlo and bootstrap are underperformers, so they are not recommended as general-purpose resampling methods. At the same time, no resampling method was found to be uniformly better than the others.
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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