Large-Scale Machine Learning Modeling of Generational Differences in Relationship Stability Factors

Abstract

This study aimed to identify and compare generational differences in the predictors of relationship stability using a large-scale machine-learning framework applied to adults in the United States. This cross-sectional study analyzed data from 4,812 adults representing Baby Boomers, Generation X, Millennials, and Generation Z. Participants completed a multidimensional online assessment capturing psychological, relational, socioeconomic, and digital-behavioral variables. Quantitative scales measured communication clarity, emotional support, attachment anxiety, conflict recovery time, financial stress, and additional relational factors, while open-ended items provided qualitative textual data. Preprocessing included imputation, normalization, categorical encoding, and NLP-based embedding of narrative responses. Machine-learning models—including gradient-boosted trees and random forest algorithms—were used to predict relationship stability, with performance evaluated via accuracy, AUC, precision, recall, and F1-scores. SHAP analysis was conducted to interpret feature importance globally and within generational subgroups. Machine-learning models achieved strong predictive performance across generations (AUC range: 0.84–0.92). SHAP values revealed significant generational differences: communication clarity was the strongest predictor for Baby Boomers and Generation X, financial stress and attachment anxiety were dominant predictors for Millennials and Gen Z, and digital comparison exposure showed sharply increasing influence from older to younger cohorts. Relationship length exhibited high predictive value for older generations but minimal influence among younger adults. Across all models, higher emotional support and shorter conflict recovery time significantly increased predicted relationship stability, while elevated financial stress and attachment anxiety significantly reduced stability probabilities. Generational cohorts differ markedly in the factors that predict relationship stability, with younger adults exhibiting heightened sensitivity to financial strain, emotional insecurity, and digital comparison pressures. Machine-learning modeling reveals that relationship stability is not governed by universal predictors but instead emerges from generationally distinct psychological, socioeconomic, and technological influences.