摘要
由一名新闻记者兼机器人与机器学习每日新闻的工作人员新闻编辑-关于人工智能的详细数据已经呈现。根据NewsRx Journ Alists从斯洛文尼亚马里博尔的新闻报道，研究表明，“形成最佳的同行群体是多元评估的关键步骤。”我们的新闻记者从马里博尔大学的研究中获得了一句话：“除其他外，传统的回归方法需要先确定最优的同行选择准则集和最优的群体规模,由于企业的复杂性和多样性,在选择准则上没有普遍适用的封闭和互补规则集,本文只研究了非上市公司,为了解决这一问题，我们通过严格的回归分析，建立了一个定制的基准模型，旨在将其结果与我们独特的方法并列，丰富对非上市公司交易动力学的理解，将线性回归方法的性能发挥到最大，使用了关于选择标准的各种数据集（完全以及F-和NCA优化）。使用超过20,000笔私营公司交易的样本，采用多种预测误差措施（强调偏差和准确性）以及直接预测优势来评估模型性能。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on artificial intelligence have bee n presented. According to news reporting from Maribor, Slovenia, by NewsRx journ alists, research stated, “Forming optimal peer groups is a crucial step in multi plier valuation.” Our news correspondents obtained a quote from the research from University of Ma ribor: “Among others, the traditional regression methodology requires the defini tion of the optimal set of peer selection criteria and the optimal size of the p eer group a priori. Since there exists no universally applicable set of closed a nd complementary rules on selection criteria due to the complexity and the diver se nature of firms, this research exclusively examines unlisted companies, rende ring direct comparisons with existing studies impractical. To address this, we d eveloped a bespoke benchmark model through rigorous regression analysis. Our aim was to juxtapose its outcomes with our unique approach, enriching the understan ding of unlisted company transaction dynamics. To stretch the performance of the linear regression method to the maximum, various datasets on selection criteria (full as well as F- and NCA-optimized) were employed. Using a sample of over 20 ,000 private firm transactions, model performance was evaluated employing multip lier prediction error measures (emphasizing bias and accuracy) as well as predic tion superiority directly.”
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