Advances in Machine Learning for the Behavioral Sciences
It provides an introductory overview for researchers in behavioral sciences, but it is incremental as it primarily reviews existing methods.
The paper reviews recent and classical machine learning algorithms, focusing on tasks like learning from tabular, behavioral, and textual data, with applications in behavioral sciences and practical guidance on software implementations.
The areas of machine learning and knowledge discovery in databases have considerably matured in recent years. In this article, we briefly review recent developments as well as classical algorithms that stood the test of time. Our goal is to provide a general introduction into different tasks such as learning from tabular data, behavioral data, or textual data, with a particular focus on actual and potential applications in behavioral sciences. The supplemental appendix to the article also provides practical guidance for using the methods by pointing the reader to proven software implementations. The focus is on R, but we also cover some libraries in other programming languages as well as systems with easy-to-use graphical interfaces.