Tree-based machine learning methods for survey research.

Autor/inn/en	Kern, Christoph; Klausch, Thomas; Kreuter, Frauke
Titel	Tree-based machine learning methods for survey research. Gefälligkeitsübersetzung: Baumbasierte Methoden des maschinellen Lernens für die Umfrageforschung.
Quelle	In: Survey research methods, 13 (2019) 1, S. 73-93 PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	1864-3361
DOI	10.18148/srm/2019.v1i1.7395
Schlagwörter	Sozioökonomisches Panel; Künstliche Intelligenz; Lernmethode; Genauigkeit; Prognose; Prognosemodell
Abstract	"Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys." (Author's abstract, IAB-Doku).
Erfasst von	Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg
Update	2020/3