摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx记者在德国慕尼黑发布的新闻报道，研究表明：“基于机器学习的数据分析可以补充或取代DA TA的经典统计分析。然而，在某些学科，如教育研究，研究经常局限于小数据集，这就提出了一些关于偏见和巧合的积极结果的问题。”新闻记者引用了Ludwig-Maximilians-Un Iversitat Munchen的研究，“在这项研究中，我们提出了一种改进的方法，用于评估基于机器学习的小D数据集二进制分类的性能。该方法包括非参数置换测试，作为量化结果推广到新数据的概率的方法。此外，我们发现，重复嵌套交叉验证几乎没有偏差，YIE LDS的可靠结果仅略微依赖于机会。考虑到几个评价指标的优点，我们建议结合多个评价指标来训练和评价机器学习分类器。在两个类别同等重要的具体情况下，马修斯相关系数nt表现出最低的偏差和巧合良好结果的机会。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Munich, German y, by NewsRx journalists, research stated, “Classical statistical analysis of da ta can be complemented or replaced with data analysis based on machine learning. However, in certain disciplines, such as education research, studies are freque ntly limited to small datasets, which raises several questions regarding biases and coincidentally positive results.” The news reporters obtained a quote from the research from Ludwig-Maximilians-Un iversitat Munchen, “In this study, we present a refined approach for evaluating the performance of a binary classification based on machine learning for small d atasets. The approach includes a non-parametric permutation test as a method to quantify the probability of the results generalising to new data. Furthermore, w e found that a repeated nested cross-validation is almost free of biases and yie lds reliable results that are only slightly dependent on chance. Considering the advantages of several evaluation metrics, we suggest a combination of more than one metric to train and evaluate machine learning classifiers. In the specific case that both classes are equally important, the Matthews correlation coefficie nt exhibits the lowest bias and chance for coincidentally good results.”
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