Advances in Machine Learning for the Behavioral Sciences

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.

• Publication year

  2019

• Venue

  American Behavioral Scientist

• Publication date

  2019-07-24

• Fields of study

  Mathematics, Computer Science, Psychology

• Identifiers
  DOI 10.1177/0002764219859639arXiv 1911.03249
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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