Fig. 2: Cross-validation ratios (CVR) of the significant predictive baseline variables overlapping across the models. | Schizophrenia

Fig. 2: Cross-validation ratios (CVR) of the significant predictive baseline variables overlapping across the models.

From: Multivariable prediction of functional outcome after first-episode psychosis: a crossover validation approach in EUFEST and PSYSCAN

Fig. 2

The green lines reflect the 95% confidence threshold (CVR = ± 2). a EUFEST classifier = internally cross-validated SVM model on EUFEST data. b PSYSCAN classifier = internally cross-validated SVM model on PSYSCAN data. c Leave-site-out classifier = inner pooled/outer leave-site-out cross-validated SVM model on the merged dataset (sites that included < 10 participants were excluded). Positive CV ratios indicate that the variable contributes to the classification of the poor outcome label, whereas negative CV ratios indicate the opposite. The absolute CV ratio values indicate how strongly the variable affects the decision towards the outcome label (i.e., a variable with a higher absolute CV ratio drives the decision more strongly towards the classification of the outcome label than a variable with a lower absolute CV ratio).

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