Fig. 2.

Discrimination and importance scores across. Boxen plots of pooled test fold AUROCs from cross-validation for all classifiers show best prediction from combined predictors compared to each predictor individually (a). Median per-predictor permutation feature importance (PFI) scores (b, left) across folds for all classifiers gives sex and schizophrenia polygenic risk score (PRS_SZ) as the strongest predictors, while per-group importance (b, right) shows PRS_SZ is similar in importance to all clinical and demographic predictors taken together. Importance scores do not indicate direction of effect, for which estimates are given in Appendix B (Table S3). Average receiver operator characteristic (ROC) curves show similar average discrimination across classifiers in models using only PRS_SZ (c) or all clinical/demographic predictors (d) individually or in combination (e). LR: logistic regression, LASSO: least absolute shrinkage and selection operator, RF: random forest, SVM: support vector machine, RBF: radial basis function, XGB: XGBoost, NN: neural network.

Discrimination and importance scores across. Boxen plots of pooled test fold AUROCs from cross-validation for all classifiers show best prediction from combined predictors compared to each predictor individually (a). Median per-predictor permutation feature importance (PFI) scores (b, left) across folds for all classifiers gives sex and schizophrenia polygenic risk score (PRS_SZ) as the strongest predictors, while per-group importance (b, right) shows PRS_SZ is similar in importance to all clinical and demographic predictors taken together. Importance scores do not indicate direction of effect, for which estimates are given in Appendix B (Table S3). Average receiver operator characteristic (ROC) curves show similar average discrimination across classifiers in models using only PRS_SZ (c) or all clinical/demographic predictors (d) individually or in combination (e). LR: logistic regression, LASSO: least absolute shrinkage and selection operator, RF: random forest, SVM: support vector machine, RBF: radial basis function, XGB: XGBoost, NN: neural network.