分类模型中数据复杂性和团队特征对绩效的影响

Pub Date : 2022-01-01 DOI:10.4018/ijban.288517
V. Pungpapong, Prasert Kanawattanachai
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引用次数: 0

摘要

本文研究了数据复杂性和团队特定特征对机器学习竞赛分数的影响。我们分析了在Kaggle.com上举办的五场真实世界的二元分类比赛的数据。从标准度量、稀疏度量、类不平衡度量和基于特征的度量四个方面度量数据复杂性特征。结果表明,数据复杂度特征水平越高,机器学习模型的预测能力越低。实证结果表明,目标变量的失衡率是最重要的影响因素,且与模型的预测能力呈非线性关系。当失衡比达到一定水平时,会对预测性能产生不利影响。然而,通过团队规模、团队专业知识和提交的数量来衡量团队特定特征对团队绩效的影响,发现了不同的结果。对于高绩效团队,这些因素对团队得分没有影响。
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The Impact of Data-Complexity and Team Characteristics on Performance in the Classification Model
This article investigates the impact of data-complexity and team-specific characteristics on machine learning competition scores. Data from five real-world binary classification competitions hosted on Kaggle.com were analyzed. The data-complexity characteristics were measured in four aspects including standard measures, sparsity measures, class imbalance measures, and feature-based measures. The results showed that the higher the level of the data-complexity characteristics was, the lower the predictive ability of the machine learning model was as well. Our empirical evidence revealed that the imbalance ratio of the target variable was the most important factor and exhibited a nonlinear relationship with the model’s predictive abilities. The imbalance ratio adversely affected the predictive performance when it reached a certain level. However, mixed results were found for the impact of team-specific characteristics measured by team size, team expertise, and the number of submissions on team performance. For high-performing teams, these factors had no impact on team score.
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