Fig. 3: Feature importance based on SHAP values for the stacking ensemble machine learning model trained on 49 quantified metabolites and 25 predicted clinical parameters. | npj Metabolic Health and Disease

Fig. 3: Feature importance based on SHAP values for the stacking ensemble machine learning model trained on 49 quantified metabolites and 25 predicted clinical parameters.

From: Metabolomic-based aging clocks

Fig. 3

Features are ranked from most to least influential based on their average absolute SHAP values. Each point represents a SHAP value for an individual observation, with color indicating the original feature value (red = high, blue = low). A positive SHAP value indicates that the feature contributes to a higher predicted age, while a negative SHAP value indicates a contribution to a lower predicted age. This plot illustrates both the magnitude and direction of each feature’s impact on model predictions.

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