Fig. 7: Post-hoc interpretability using LIME approach.
From: Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection
Graphical representation of the ten most relevant features identified for the classification models by the RF, KNN-E, KNN-C and DNN algorithms using the training set of the first fold in each class. a Average spectral signature of the NT class. b Average spectral signature of the TT class. c Average spectral signature of the BV class. d Average spectral signature of the BG class. In the plot, the size of the markers represents the level of importance computed by LIME (higher size is related to higher importance). Positive coefficients are represented at the top of the chart while negative coefficients are shown at the bottom. RF is only evaluated with positive predictor importance values.
