HMKW Hochschule für Medien, Kommunikation und Wirtschaft, University of Applied Science, Berlin, Germany.
LYTiQ GmbH, Germany & Indian Institute of Information Technology Allahabad, Prayagraj, India.
PLoS One. 2019 Mar 21;14(3):e0213569. doi: 10.1371/journal.pone.0213569. eCollection 2019.
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.
基于双方的个性，机器学习在多大程度上可以预测关系的结果？在本研究中，对 192 对伴侣在完成有关其个性特征的问卷四年后，对关系满意度、冲突和分离（意图）进行了预测。使用 10x10 折交叉验证以确保线性回归模型的结果可重现。研究结果表明，机器学习技术可以提高关系质量的预测（解释方差的 37%），并且伴侣对关系的感知质量主要取决于他或她自己的个性特征。此外，还显示了不同变量集对预测的影响：伴侣和相似性效应对关系质量的预测并没有超过演员效应，而一般个性特征对关系质量的预测不如关系相关个性特征强烈。
