Additional file 14: of The parameter sensitivity of random forests

AUC performance can be predicted for high p/n data using parameters as variables. Prediction accuracy using the random forest classifier for high p/n validation data with Gini importance measures. (a) The combined validation data demonstrated a strong correlation between the predicted and observed AUC values (ρ = 0.48, p < 10−20) and a ρ c value of 0.33. (b) The relative order of Gini importance for the combined data was sampsize followed by m try and lastly, n tree . (TIFF 68 kb)