Fig. 3
From: Integration of metabolomics, lipidomics and clinical data using a machine learning method
Random Forest (RF) classification approach for the determination of class error (how well the PPAR-pan dose level is predicted) and the selection of variables (which variables contribute to PPAR-pan dose level prediction) in each different dataset. a Class error of metabolomic and lipidomic dataset comparing values using the full set of variables and selected variables for calculations. b The number of variables contained within each dataset (in blue) and the number of metabolites after variable selection (in orange). For example, the number of total acyl-carnitines is 40 (in blue) and only four were selected (in orange)