论文标题
在机器学习任务中使用专家的意见
Using Experts' Opinions in Machine Learning Tasks
论文作者
论文摘要
在机器学习任务中，尤其是在预测任务中，科学家倾向于仅依靠可用的历史数据并忽略未经证实的见解，例如专家的意见，民意调查和赌注赔率。在本文中，我们提出了一个一般的三步框架，用于利用专家在机器学习任务中的见解，并为体育游戏预测案例研究构建四个具体模型。在案例研究中，我们选择了预测NCAA男子篮球比赛的任务，这是近年来Kaggle比赛的重点。结果强烈表明，过去模型的良好性能和高分是偶然的结果，而不是由于表现良好且稳定的模型。此外，与2019年竞赛的最高解决方案（> 0.503）相比，我们提出的模型可以取得更低的稳定结果（最佳量损失（最佳），并分别达到2017年，2018年和2019年排行榜中的最高1％，10％和1％。
In machine learning tasks, especially in the tasks of prediction, scientists tend to rely solely on available historical data and disregard unproven insights, such as experts' opinions, polls, and betting odds. In this paper, we propose a general three-step framework for utilizing experts' insights in machine learning tasks and build four concrete models for a sports game prediction case study. For the case study, we have chosen the task of predicting NCAA Men's Basketball games, which has been the focus of a group of Kaggle competitions in recent years. Results highly suggest that the good performance and high scores of the past models are a result of chance, and not because of a good-performing and stable model. Furthermore, our proposed models can achieve more steady results with lower log loss average (best at 0.489) compared to the top solutions of the 2019 competition (>0.503), and reach the top 1%, 10% and 1% in the 2017, 2018 and 2019 leaderboards, respectively.
