Turgeon Stéphanie, Lanovaz Marc J
École de psychoéducation, Université de Montréal, C.P. 6128, succursale Centre-Ville, Montreal, QC H3C 3J7 Canada.
Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Montreal, QC Canada.
Perspect Behav Sci. 2020 Nov 10;43(4):697-723. doi: 10.1007/s40614-020-00270-y. eCollection 2020 Dec.
Machine-learning algorithms hold promise for revolutionizing how educators and clinicians make decisions. However, researchers in behavior analysis have been slow to adopt this methodology to further develop their understanding of human behavior and improve the application of the science to problems of applied significance. One potential explanation for the scarcity of research is that machine learning is not typically taught as part of training programs in behavior analysis. This tutorial aims to address this barrier by promoting increased research using machine learning in behavior analysis. We present how to apply the random forest, support vector machine, stochastic gradient descent, and k-nearest neighbors algorithms on a small dataset to better identify parents of children with autism who would benefit from a behavior analytic interactive web training. These step-by-step applications should allow researchers to implement machine-learning algorithms with novel research questions and datasets.
机器学习算法有望彻底改变教育工作者和临床医生的决策方式。然而，行为分析领域的研究人员在采用这种方法以进一步加深对人类行为的理解并将该科学应用于具有实际意义的问题方面进展缓慢。研究稀缺的一个潜在原因是，机器学习通常不作为行为分析培训项目的一部分进行教授。本教程旨在通过推动在行为分析中更多地使用机器学习进行研究来克服这一障碍。我们展示了如何在一个小数据集上应用随机森林、支持向量机、随机梯度下降和k近邻算法，以更好地识别那些将从行为分析交互式网络培训中受益的自闭症儿童的家长。这些逐步的应用应能让研究人员用新颖的研究问题和数据集来实施机器学习算法。
