Advances in Machine Learning for the Behavioral Sciences

TL;DR

This paper reviews recent advances and classical algorithms in machine learning, emphasizing applications in behavioral sciences and providing practical guidance on software tools, mainly in R.

Contribution

It offers a comprehensive overview of ML methods applicable to behavioral data and practical guidance for implementation in behavioral science research.

Findings

Summarizes recent developments in ML for behavioral sciences

Provides practical guidance on software implementations

Highlights applications of ML in analyzing behavioral data

Abstract

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.