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%) |