KMAN Publication Inc. (KMANPUB) Psychology of Woman Journal 3041-8615 7 Serial Number 27 2026 05 01 Predicting Women's Psychological Flourishing Using XGBoost: The Relative Importance of Self-Compassion, Resilience, Emotional Intelligence, and Social Support Predicting Women's Psychological Flourishing Using XGBoost: The Relative Importance of Self-Compassion, Resilience, Emotional Intelligence, and Social Support 1 13 10.61838/kman.pwj.5446 EN Journal Article 2026 01 10 <table> <tbody> <tr> <td> <p><strong>Objective:</strong> The present study aimed to predict psychological flourishing among women using the Extreme Gradient Boosting (XGBoost) machine-learning algorithm and to determine the relative importance of self-compassion, resilience, emotional intelligence, and perceived social support as predictors of flourishing.</p> <p><strong>Methods and Materials:</strong> This cross-sectional predictive study was conducted among 642 adult women residing in Canada. Participants were recruited through online platforms and community networks and completed a battery of standardized self-report measures assessing psychological flourishing, self-compassion, resilience, emotional intelligence, and perceived social support. After data screening and preprocessing, descriptive statistics and Pearson correlations were calculated. The dataset was randomly divided into training (80%) and testing (20%) subsets. XGBoost was employed as the primary predictive model due to its ability to capture complex nonlinear relationships and interactions among predictors. Hyperparameter tuning was performed using five-fold cross-validation and grid-search optimization procedures. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and explained variance. Feature importance analysis and SHAP (Shapley Additive Explanations) values were used to determine the relative contribution and interpretability of each predictor.</p> <p><strong>Findings:</strong> All predictor variables demonstrated significant positive correlations with psychological flourishing. The XGBoost model exhibited strong predictive performance, explaining 82.1% of the variance in flourishing scores on the test dataset (R² = .821), with low prediction errors (RMSE = 3.18; MAE = 2.47). Feature importance analysis revealed that resilience was the most influential predictor (34.1%), followed by self-compassion (28.6%), emotional intelligence (22.3%), and social support (15.0%). SHAP analyses confirmed that all predictors exerted positive effects on flourishing, with resilience demonstrating the strongest impact on model predictions. The findings indicated that internal psychological strengths contributed more substantially to flourishing than external support resources.</p> </td> </tr> </tbody> </table> <p><strong>Conclusion:</strong> The findings suggest that women’s psychological flourishing can be accurately predicted using machine-learning approaches and is primarily influenced by resilience, self-compassion, emotional intelligence, and social support. Resilience emerged as the strongest determinant of flourishing, highlighting the importance of adaptive coping capacities in promoting optimal psychological functioning. Interventions aimed at enhancing flourishing among women should prioritize the development of resilience and self-compassion while also strengthening emotional intelligence and supportive interpersonal relationships.</p> https://journals.kmanpub.com/index.php/psywoman/article/download/5446/10731