Table 2 Prediction performance benchmarking for the prediction task of “Multi Target Transfer Learning" on the test set.

KLU

28,056

18.77

11.96

11.37

KAA

28,171

5.234

2.978

2.821

BgOptb

28,163

0.988

0.279

0.251

Deltae

28,155

0.850

0.135

0.120

Encut

28,108

246.25

76.99

83.09

Ehull

27,297

0.131

0.055

0.050

Magoszi

25,844

1.225

0.438

0.405

Magout

25,357

1.176

0.393

0.369

Eps

25,150

3.829

1.462

1.304

PPF

16,250

650.5

543.1

508.6

NPF

16,250

658.1

546.3

493.0

Pem300k

16,763

1.918

1.293

1.111

Nem300k

16,760

1.918

1.282

1.183

PSB

14,439

163.30

68.34

60.53

NSB

14,144

108.69

57.83

53.32

Meps

11,349

4.905

1.926

1.832

MaxM

10,963

285.32

72.66

65.69

MinM

10,930

40.89

24.85

23.51

ETC11

10,839

81.66

37.35

34.03

ETC12

10,759

44.96

19.05

17.15

ETC13

10,846

42.54

15.65

13.90

ETC22

10,832

84.06

36.99

32.13

ETC33

10,856

84.12

38.93

33.89

ETC44

9986

29.55

17.24

14.76

ETC55

9755

26.61

14.90

11.71

ETC66

9739

27.59

15.83

13.81

BulkKV

10,743

49.11

11.83

11.01

ShearGV

10,209

24.28

11.90

11.11

BgMbj

7296

1.911

0.555

0.508

Spillage

3866

0.501

0.379

0.371

SLME

3006

9.439

7.193

6.877

MaxIrM

2302

426.0

108.2

104.6

MinIrM

2268

66.16

49.90

47.14

PMDiEl

2126

5.757

3.221

3.070

PMDi

2126

6.977

3.931

3.761

PMDiIo

2126

2.577

0.847

0.791

PMEij

1123

0.520

0.436

0.415

PMDij

689

46.47

24.43

22.32

Exfoli

557

62.93

59.37

48.11