From: Building efficient fuzzy regression trees for large scale and high dimensional problems
Prob. | DFRT | RF | ||
---|---|---|---|---|
Training ± s.d. | Test ± s.d. | Training ± s.d. | Test ± s.d. | |
Window2 | 24.3263 ± 0.18 | 24.3978 ± 0.90 | 22.9981 ± 3.02 | 29.9958 ± 5.72 |
Window3 | 24.2976 ± 0.18 | 24.3853 ± 0.91 | 23.1057 ± 3.43 | 29.0574 ± 5.35 |
Window4 | 24.2841 ± 0.18 | 24.3730 ± 0.91 | 21.3935 ± 1.31 | 28.9095 ± 5.13 |
Window5 | 24.2833 ± 0.18 | 24.3732 ± 0.91 | 22.1959 ± 2.85 | 28.8784 ± 4.95 |
Window6 | 24.2834 ± 0.18 | 24.3733 ± 0.91 | 22.5899 ± 3.53 | 29.0761 ± 5.52 |
Window7 | 24.2836 ± 0.18 | 24.3738 ± 0.91 | 21.9378 ± 2.56 | 29.7301 ± 4.78 |
Window8 | 24.2841 ± 0.18 | 24.3742 ± 0.91 | 21.0925 ± 2.64 | 31.1765 ± 5.26 |
Window9 | 24.2807 ± 0.18 | 24.3689 ± 0.91 | 21.8789 ± 2.82 | 28.9022 ± 4.94 |
\(f_1\) | 15.0433 ± 0.03 | 16.6537 ± 0.18 | 17.6090 ± 0.48 | 15.0783 ± 0.84 |
\(f_{19}\) | 6.5671 ± 0.02 | 7.3154 ± 0.11 | 6.6059 ± 0.08 | 6.8696 ± 0.09 |
\(f_2\) | 31.3784 ± 0.02 | 32.0971 ± 0.14 | 32.2631 ± 0.28 | 29.1347 ± 0.75 |
\(f_4\) | 428.8752 ± 0.47 | 429.8814 ± 3.39 | 426.1867 ± 0.74 | 427.2893 ± 3.40 |