From: Examining characteristics of predictive models with imbalanced big data
Learners | Ratios | Case study 1: ECBDL’14 features subsets | Case study 2: POST features subsets | ||||||
---|---|---|---|---|---|---|---|---|---|
60 | 120 | ALL | 5 | 10 | 20 | 36 | ALL | ||
(a) GBT | (40:60) | 0.4884 | 0.4891 | 0.4947 | 0.8188 | 0.8636 | 0.8503 | 0.8540 | 0.7527 |
(45:55) | 0.4873 | 0.4883 | 0.4905 | 0.8109 | 0.8570 | 0.7795 | 0.8468 | 0.7541 | |
(50:50) | 0.4671 | 0.4737 | 0.4777 | 0.8423 | 0.8397 | 0.6933 | 0.8233 | 0.7452 | |
(65:35) | 0.3423 | 0.3460 | 0.3470 | 0.8240 | 0.8706 | 0.5977 | 0.5935 | 0.5568 | |
(75:25) | 0.2175 | 0.2285 | 0.2243 | 0.8180 | 0.8651 | 0.3685 | 0.2949 | 0.5133 | |
(90:10) | 0.0384 | 0.0382 | 0.0353 | 0.8183 | 0.4815 | 0.4046 | 0.3273 | 0.3415 | |
(b) RF | (40:60) | 0.4529 | 0.4460 | 0.4218 | 0.0334 | 0.8789 | 0.8678 | 0.8793 | 0.8393 |
(45:55) | 0.4784 | 0.4788 | 0.4788 | 0 | 0.8789 | 0.8563 | 0.7515 | 0.8226 | |
(50:50) | 0.4496 | 0.4470 | 0.4412 | 0 | 0.8820 | 0.0140 | 0.0830 | 0.0382 | |
(65:35) | 0.2051 | 0.1799 | 0.1364 | 0.0798 | 0 | 0.0762 | 0.1237 | 0.1940 | |
(75:25) | 0.0750 | 0.0469 | 0.0190 | 0.8547 | 0.8539 | 0.8532 | 0.8497 | 0.8596 | |
(90:10) | 0 | 0 | 0 | 0.8966 | 0.9061 | 0.8196 | 0.7375 | 0 | |
(c) LR | (40:60) | 0.4452 | 0.4503 | 0.4568 | 0.8770 | 0.6210 | 0.8302 | 0.4697 | 0.3113 |
(45:55) | 0.4663 | 0.4701 | 0.4754 | 0.8773 | 0.6920 | 0.5852 | 0.4620 | 0.5935 | |
(50:50) | 0.4680 | 0.4731 | 0.4758 | 0.8779 | 0.4988 | 0.4487 | 0.5528 | 0.4920 | |
(65:35) | 0.3562 | 0.3712 | 0.3740 | 0.8787 | 0.6106 | 0.5018 | 0.5030 | 0.5667 | |
(75:25) | 0.2131 | 0.2328 | 0.2381 | 0.8789 | 0.4026 | 0.3307 | 0.3217 | 0.4750 | |
(90:10) | 0.0153 | 0.0196 | 0.0230 | 0.7514 | 0.1099 | 0.1532 | 0.2036 | 0.1062 |