Fig. 5. Feature importances in the fully integrated IRON radiogenomic model.

Importances of the features used by the predictive models. The first (blue) heatmap illustrates the selection frequency. The heatmap shows the number of times that a given feature was selected in a model. The different columns correspond to different models with increasing, cumulative numbers of input features. As the optimisation is repeated five times, the range of the selection frequency is 0–15 (three algorithms in the ensemble times five repetitions). The first (green) heatmap illustrates the averaged, normalised feature importances for the elastic net and random forest components of the models. Importances are defined from the feature coefficients for the elastic net regression, and from impurity-based Gini importances for random forest. Source data are provided as a Source Data file.