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Table 2 Results on different data sets.

From: Learning accurate and interpretable models based on regularized random forests regression

Numbers after ± are standard deviation. SVR is support vector regression.

 

Random Forests

Our Approach

SVR

Stockori Flowing Time

R 2

0.54 ± 0.00

0.45 ± 0.05

0.28 ± 0.03

Number of Rules Selected

66020 ± 187

348 ± 33

NA

Number of Features Used in a Rule

8.8 ± 1.9

7.5± 1.74

NA

Number of Features Selected

149 ± 0

135 ± 31

149 ± 0

Parkinson's Telemonitoring

R 2

0.15 ± 0.02

0.06 ± 0.02

0.17 ± 0.02

Number of Rules Selected

644789 ± 414

3796 ± 0

NA

Number of Features Used in a Rule

9.72± 2.14

7.4 ± 1.86

NA

Number of Features Selected

19 ± 0

19 ± 0

19 ± 0

Breast Cancer Wisconsin (Prognostic)

R 2

0.04 ± 0.02

-0.19 ± 0.16

-0.04 ± 0.04

Number of Rules Selected

43907 ± 58

126 ± 2

NA

Number of Features Used in a Rule

7 ± 3

3 ± 1.49

NA

Number of Features Selected

32 ± 0

31 ± 1

32 ± 0

Relative location of CT slices on axial axis

R 2

0.92 ± 0.01

0.77 ± 0.09

0.26 ± 0.00

Number of Rules Selected

172984 ± 143

901 ± 15

NA

Number of Features Used in a Rule

12 ± 3.12

8 ± 2.53

NA

Number of Features Selected

384 ± 0

20 ±± 5

384 ± 0

Seacoast

R 2

0.64 ± 0.02

0.59 ± 0.10

-0.19 ± 0.00

Number of Rules Selected

120771 ± 161

385 ≤ 5

NA

Number of Features Used in a Rule

14 ±± 3

6 ± 1.91

NA

Number of Features Selected

16 ± 0

16 ± 0

16 ± 0

TCGA Glioblastoma multiforme

R 2

0.04 ± 0.01

-1.94 ± 0.67

-0.09 ± 0.00

Number of Rules Selected

53539 ± 31344

279 ± 6

NA

Number of Features Used in a Rule

3 ± 2

2 ± 1

NA

Number of Features Selected

12042 ± 0

2 ± 1

12042 ± 0