Fig. 3. Predictive decomposition of schizophrenia symptom profile.

a Item groups: a parsimony-inducing learning algorithm was used to search through the array of questionnaire items and extract the most parsimonious subsets of items for predicting schizophrenia severity. Profiles of the classifier coefficients of the PANSS items are plotted on the y axis while the decreasing parsimony constraint of this statistical model (here represented as the increasing number of items automatically selected) is plotted on the x axis. The departing lines indicate changes in the subset of selected items (i.e., the active set). The color of each line shows the group affiliation of each questionnaire item. b Prediction accuracy retraces how prediction performance increases step-by-step as the seven identified item subsets are added to the model. Each colored point represents a predictive model including a specific number of selected items. c Relative item importance: item importance in the active coefficients as the parsimony constraint becomes more lenient (left to right). This panel thus represents the relative importance of each item (y axis) as more variables are included in the model (x axis, from left to right). In sum, the results emphasize that using a model including only eleven PANSS items, schizophrenia severity was predicted with an accuracy only 15% below the accuracy obtained with the model including the 30 PANSS items indicating a very high predictive power for these eleven items