使用机器学习技术预测大学篮球比赛结果：一些结果和经验教训
Predicting college basketball match outcomes using machine learning techniques: some results and lessons learned
KU Leuven（鲁汶大学）
摘要：
大多数现有的预测NCAAB比赛的工作已经在统计方面。相信机器学习技术的能力，特别是分类学习器，以揭示特征的重要性并学习它们的关系，我们评估了一些不同的范例。在本文中，我们总结了我们的工作，指出属性似乎比模型更重要，而且预测质量似乎有一个上限。
英文摘要：
Most existing work on predicting NCAAB matches has been developed in a statistical context. Trusting the capabilities of ML techniques, particularly classification learners, to uncover the importance of features and learn their relationships, we evaluated a number of different paradigms on this task. In this paper, we summarize our work, pointing out that attributes seem to be more important than models, and that there seems to be an upper limit to predictive quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
