Semantic Scholar Open Access 2020 634 sitasi

Supervised Machine Learning: A Brief Primer.

T. Jiang J. Gradus A. J. Rosellini

Lihat Sumber DOI

Abstrak

Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, and treat mental disorders and associated adverse health outcomes (e.g., suicidal behavior). Machine learning offers new tools to overcome challenges for which traditional statistical methods are not well-suited. This paper provides an overview of machine learning with a specific focus on supervised learning (i.e., methods that are designed to predict or classify an outcome of interest). Several common supervised learning methods are described, along with applied examples from the published literature. We also provide an overview of supervised learning model building, validation, and performance evaluation. Finally, challenges in creating robust and generalizable machine learning algorithms are discussed.

Topik & Kata Kunci

Psychology Medicine

Penulis (3)

T. Jiang

J. Gradus

A. J. Rosellini

Format Sitasi

APA MLA BibTeX

Jiang, T., Gradus, J., Rosellini, A.J. (2020). Supervised Machine Learning: A Brief Primer.. https://doi.org/10.1016/j.beth.2020.05.002

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.beth.2020.05.002

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 634×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.beth.2020.05.002
Akses: Open Access ✓