Fig. 3
From: Pesticides, cancer, and oxidative stress: an application of machine learning to NHANES data
Random Forest prediction model for evaluating the importance of oxidative stress biomarkers and pesticides for cancer A The result of variable importance. Pesticide 1:2,4-D; Pesticide 2:4-fluoro-3-phenoxybenzoic acid; Pesticide 3: 3-phenoxybenzoic acid; Pesticide 4: Oxypyrimidine; Pesticide 5: Paranitrophenol; Pesticide 6: Dichlorovnl-dimeth prop carboacid. B ROC curve of a stochastic forest model optimized by hyperparameters. C SHAP value interpretation based on random forest model