Huang, Barbara Boutros, Paul Additional file 2: of The parameter sensitivity of random forests AUC results for low p/n data. Low p/n results for prediction accuracy using AUC as the performance metric for non-cross-validation results, 10-fold cross-validation and stratified 10-fold cross-validation. Ranks indicate the relative performance of different models with lower ranks representing higher performing models i.e., a rank of 1 is the best model. The default settings (n tree  = 500, m try  = 3, sampsize = 720) are found on row 1502 of the table. (CSV 116 kb) Machine-learning;Random forest;Parameterization;Computational biology;Ensemble methods;Optimization;Microarray;SeqControl 2016-09-01
    https://springernature.figshare.com/articles/dataset/Additional_file_2_of_The_parameter_sensitivity_of_random_forests/4409744
10.6084/m9.figshare.c.3626282_D2.v1