Not seeing the wood for the trees: Influences on random forest accuracy

IF 2.4 4区 管理学 Q3 BUSINESS
Chris Hand, Elena Fitkov-Norris
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引用次数: 0

Abstract

Machine learning classifiers are increasingly widely used. This research note explores how a particular widely used classifier, the Random Forest, performs when faced with samples which are imbalanced and noisy data. Both are known to affect accuracy, but if their effects are independent or not has not been explored. Based on an experiment using synthetic data generated for the study we find that the effects of noise and sample balance interact with each other; classification accuracy is worse when faced with both noisy data and sample imbalance. This has implications for the use of RF in market research, but also for how methods to address either sample imbalance or noise are assessed.
只见树木不见森林随机森林准确性的影响因素
机器学习分类器的应用越来越广泛。本研究报告探讨了一种广泛使用的分类器--随机森林--在面对不平衡样本和噪声数据时的表现。众所周知,这两种情况都会影响准确性,但它们的影响是否相互独立,还没有进行过探讨。基于使用为本研究生成的合成数据进行的实验,我们发现噪声和样本平衡的影响是相互影响的;当同时面对噪声数据和样本不平衡时,分类准确率会降低。这不仅对射频技术在市场研究中的应用有影响,而且对如何评估解决样本不平衡或噪声的方法也有影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.00
自引率
6.70%
发文量
38
期刊介绍: The International Journal of Market Research is the essential professional aid for users and providers of market research. IJMR will help you to: KEEP abreast of cutting-edge developments APPLY new research approaches to your business UNDERSTAND new tools and techniques LEARN from the world’s leading research thinkers STAY at the forefront of your profession
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