Feature Sensitivity Mapping of Adolescents’ Identity Development: An Interpretable Machine Learning Study in Brazil

Objective: The objective of this study was to identify and quantify the relative and interactive contributions of psychosocial, relational, cultural, and individual predictors of adolescents’ identity development using feature sensitivity mapping within an interpretable machine learning framework. Methods and Materials: This cross-sectional study was conducted among 1,032 adolescents aged 14–18 years recruited from public and private secondary schools in three major metropolitan regions of Brazil using multi-stage cluster sampling. Participants completed validated measures assessing identity development dimensions, parental autonomy support, peer attachment quality, emotional self-regulation, future orientation, academic self-efficacy, social competence, and demographic characteristics. Data were analyzed using an ensemble of gradient boosting and random forest models with ten-fold cross-validation and Bayesian hyperparameter optimization. Model interpretability was achieved through SHAP-based feature sensitivity mapping, partial dependence plots, and accumulated local effects analysis to examine nonlinear and interactive predictor effects. Findings: The machine learning models explained substantial variance in identity outcomes, with the highest performance observed for identity commitment (R² = .61), followed by exploration in breadth (R² = .57), exploration in depth (R² = .55), and ruminative exploration (R² = .49). Parental autonomy support, peer attachment quality, emotional self-regulation, future orientation, academic self-efficacy, and social competence emerged as the most influential predictors across identity dimensions. Emotional self-regulation demonstrated the strongest protective effect against ruminative exploration, while parental autonomy support and peer attachment quality exhibited the highest positive contributions to identity commitment. Conclusion: Adolescents’ identity development operates as a nonlinear, interactive system in which relational security, self-regulation, and motivational resources play central roles, and interpretable machine learning provides a powerful framework for advancing developmental theory and intervention design.