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Table 7 Average MSEs and their standard deviation obtained by DFRT and RF on the training and test sets for the real world and synthetic datasets

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
  1. The best results for each dataset have been emphasized in italic font
  2. The results have to be multiplied by \(10^{21}\), \(10^{22}\), \(10^4\) and \(10^{31}\) for \(f_1\), \(f_{19}\), \(f_2\) and \(f_4\) respectively