Gender effects and racial biases in mate selection as revealed by machine learning.

ABSTRACT: This study investigates the potential role of machine learning (ML) technology for predicting a match, or mutual interest, in the context of speed dating. Modern machine learning technologies (light gradient boosting machine - lgbm, random forest, logistic regression, stochastic gradient descent, k nearest neighbour), exhaustively combined with feature selection methods (filter-based association, filter-based prediction, embedded lgbm, embedded linear, redundancy aware step up wrapper), were applied to a speed dating dataset, and tasked with predicting a match (mutual interest from speed dating participants). Our analysis employed public-domain ML software combined with a public-domain dataset, supporting reproducibility of study findings. Results indicate that ML models can predict a matc

SUBMITTER: Hastings-James A 

PROVIDER: S-EPMC12660853 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature