Open AccessMachine learning meets partner matching: Predicting the future relationship quality based on personality traits
Author(s) -
Inga Großmann,
André Hottung,
Artus Krohn-Grimberghe
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0213569
Subject(s) - personality , big five personality traits , quality (philosophy) , similarity (geometry) , matching (statistics) , psychology , big five personality traits and culture , variance (accounting) , regression analysis , alternative five model of personality , social psychology , machine learning , computer science , artificial intelligence , statistics , mathematics , philosophy , accounting , epistemology , business , image (mathematics)
To what extent is it possible to use machine learning to predict the outcome of a relationship, based on the personality of both partners? In the present study, relationship satisfaction, conflicts, and separation (intents) of 192 partners four years after the completion of questionnaires concerning their personality traits was predicted. A 10x10-fold cross-validation was used to ensure that the results of the linear regression models are reproducible. The findings indicate that machine learning techniques can improve the prediction of relationship quality (37% of variance explained), and that the perceived relationship quality of a partner is mostly dependent on his or her own individual personality traits. Additionally, the influences of different sets of variables on predictions are shown: partner and similarity effects did not incrementally predict relationship quality beyond actor effects and general personality traits predicted relationship quality less strongly than relationship-related personality.