Extended Data Fig. 3: Permutation feature importance.

Permutation feature importance is a shuffle-based technique to determine the contribution of features to classification performance⁶⁷. Since the features were correlated, a dendrogram-based clustering was used to identify clusters of features (distance threshold = 1). Features within a cluster were permuted jointly to generate shuffled datasets (n = 100) which were then evaluated using the classifier trained on the original dataset. The decrease in performance of the shuffled datasets provides a measure of the feature’s contribution to classifier performance. a, Adjacency matrix based on Spearman correlation between spectral features. Hotter colors indicate a positive correlation. b, Dendrogram-based clustering of features. c, Difference in Area under ROC curve between classifier trained on original dataset and shuffled datasets (n = 100).