Visual analysis is the most commonly used method for interpreting data from single-case designs, but levels of interrater agreement remain a concern. Although structured aids to visual analysis such as the dual-criteria (DC) method may increase interrater agreement, the accuracy of the analyses may still benefit from improvements. Thus, the purpose of our study was to (a) examine correspondence between visual analysis and models derived from different machine learning algorithms, and (b) compare the accuracy, Type I error rate and power of each of our models with those produced by the DC method. We trained our models on a previously published dataset and then conducted analyses on both nonsimulated and simulated graphs. All our models derived from machine learning algorithms matched the interpretation of the visual analysts more frequently than the DC method. Furthermore, the machine learning algorithms outperformed the DC method on accuracy, Type I error rate, and power. Our results support the somewhat unorthodox proposition that behavior analysts may use machine learning algorithms to supplement their visual analysis of single-case data, but more research is needed to examine the potential benefits and drawbacks of such an approach.

中文翻译：

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机器学习分析单例数据：概念证明。

视觉分析是解释单案例设计中数据的最常用方法，但是相互同意的程度仍然值得关注。尽管诸如双标准（DC）方法之类的可视化分析的结构化辅助工具可能会增加相互间的一致性，但分析的准确性仍可能会受益于改进。因此，我们的研究目的是（a）检查视觉分析与源自不同机器学习算法的模型之间的对应关系，以及（b）将我们每个模型的准确性，第一类错误率和功效与由模型产生的模型进行比较。 DC法。我们在先前发布的数据集上训练了模型，然后对非模拟图和模拟图进行了分析。我们所有来自机器学习算法的模型都比DC方法更符合视觉分析人员的解释。此外，机器学习算法在准确性，I类错误率和功耗方面都优于DC方法。我们的结果支持行为分析师可能会使用机器学习算法来补充其对单例数据的可视化分析的观点，这有些不合常规，但需要更多的研究来研究这种方法的潜在利弊。