From: Optimally splitting cases for training and testing high dimensional classifiers
Optimal number to training set | ||||
---|---|---|---|---|
n = 200 | ||||
Effect = 0.5 | Effect = 1.0 | Effect = 1.5 | Effect = 2.0 | |
DEG = 50 | 170 (86%) | 70+ (>99%) | 30+ (>99%) | 20+ (>99%) |
DEG = 10 | 150 (64%) | 130 (94%) | 100 (99%) | 60+ (>99%) |
DEG = 1 | 10 (52%) | 150 (69%) | 120 (77%) | 80 (84%) |
n = 100 | ||||
DEG = 50 | 70 (64%) | 80 (>99%) | 30+ (>99%) | 20+ (>99%) |
DEG = 10 | 10 (55%) | 80 (91%) | 70 (99%) | 40+ (>99%) |
DEG = 1 | 10 (51%) | 40 (63%) | 80 (77%) | 70 (84%) |
n = 50 | ||||
DEG = 50 | 10 (59%) | 40 (99%) | 30+ (>99%) | 20+ (>99%) |
DEG = 10 | 10 (52%) | 40 (78%) | 40 (98%) | 40 (>99%) |
DEG = 1 | 10 (50%) | 10 (54%) | 30 (71%) | 40 (83%) |