摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新数据在一份新的报告中提供。根据NewsRx记者在丹麦哥本哈根的新闻报道，研究表明：“决策树是一种简单而实用的、可解释的机器学习工具。虽然基于树的方法只针对横断面数据设计，但我们提出了一种将决策树与时间序列建模相结合的方法，从而弥合机器学习和统计之间的差距。”这项研究的财政支持来自比勒菲尔德大学（3146）。新闻记者从哥本哈根大学的研究中得到一句话：“特别地，我们将决策树和隐马尔可夫模型结合起来，在任何时间点，本文提出了一种基于期望最大化算法的估计方法,并通过仿真实验验证了该方法的可行性,在实际数据应用中,我们使用了美国国家足球联盟(NFL)的8个赛季的数据,对c个卵巢的比赛呼叫进行了预测,如当前季度和得分。模型的状态与团队的策略相联系。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Machine Learning are pre sented in a new report. According to news reporting from Copenhagen, Denmark, by NewsRx journalists, research stated, "Decision trees constitute a simple yet po werful and interpretable machine learning tool. While tree-based methods are des igned only for cross-sectional data, we propose an approach that combines decisi on trees with time series modeling and thereby bridges the gap between machine l earning and statistics." Financial support for this research came from Universitt Bielefeld (3146). The news correspondents obtained a quote from the research from the University o f Copenhagen, "In particular, we combine decision trees with hidden Markov model s where, for any time point, an underlying (hidden) Markov chain selects the tre e that generates the corresponding observation. We propose an estimation approac h that is based on the expectation-maximisation algorithm and assess its feasibi lity in simulation experiments. In our real-data application, we use eight seaso ns of National Football League (NFL) data to predict play calls conditional on c ovariates, such as the current quarter and the score, where the model's states c an be linked to the teams' strategies."
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