Fig. 4: Variable importance results of the machine learning based classifiers. | npj Systems Biology and Applications

Fig. 4: Variable importance results of the machine learning based classifiers.

From: Machine learning and data-driven inverse modeling of metabolomics unveil key processes of active aging

Fig. 4

a Bipartite graph of the top metabolites extracted from the five machine-learning algorithms. For each algorithm, we keep the metabolites if they are identified over 15 times in the top ten metabolites over 30 hold out test sets. b SHAP summary plot of the XGBoosting algorithm in one hold out test set. It shows the contribution of the features for each instance (row of data). c The variable importance for the XGBoosting algorithm in one hold-out test set.

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