Machine learning to predict if you'll leave your partner

The life satisfaction of both partners and the woman's percentage of housework turned out to be the most important predictors of union dissolution, when scholars affiliated to Bocconi's Dondena Centre for Research on Social Dynamics and Public Policy used a machine learning (ML) technique to analyze data on 2,038 married or cohabiting couples who participated in the German Socio-Economic Panel Survey. The couples were observed, on average, for 12 years, leading to a total of 18,613 observations. In their article, newly published online on Demography, Bruno Arpino (University of Florence), Marco Le Moglie (Catholic University, Milan) and Letizia Mencarini (Bocconi), used a ML technique called Random Survival Forests (RSF) to overcome the difficulty to manage a large number of independent variables in conventional models. "A clear-cut example of the potential difficulties of considering all variables and their possible interactions concerns the'big five' personality traits," Professor Mencarini said. "To account for both partners' traits (10 variables) and all their two-way interactions (25 variables), one would need to include 35 independent variables, which would be very problematic in a regression model."