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.

中文翻译：

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教程：在行为研究中应用机器学习

机器学习算法有望彻底改变教育者和临床医生的决策方式。然而，行为分析的研究人员在采用这种方法来进一步发展他们对人类行为的理解和改进科学对具有应用意义的问题的应用方面进展缓慢。对研究稀缺的一种可能解释是，机器学习通常不作为行为分析培训计划的一部分进行教授。本教程旨在通过促进在行为分析中使用机器学习进行更多研究来解决这一障碍。我们介绍了如何应用随机森林、支持向量机、随机梯度下降、和 k 最近邻算法在小数据集上更好地识别自闭症儿童的父母，他们将从行为分析交互式网络培训中受益。这些循序渐进的应用程序应该允许研究人员使用新颖的研究问题和数据集来实施机器学习算法。