Fig. 1: Frequency of inclusion and weight of features. | npj Schizophrenia

Fig. 1: Frequency of inclusion and weight of features.

From: Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach

Fig. 1

Frequency of inclusion of a feature against its (average) weight in the model; shown for prediction of global assessment of functioning (GAF) outcome at T3, containing Positive and Negative Syndrome Scale—general, negative, and positive subscale (PANSS—Gen, Neg, and Pos), Demographic, Illness-related and lifetime psychotic experiences (CAPE) related features. A positive weight reflects that scoring higher on this feature contributes to being classified as ‘poor outcome’. For features with negative weights the opposite holds.

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