Table 8 The 8SC for models after removing heterogeneity parameters.

From: Hybrid models of sparse and robust regression to solve heterogeneity problem in black pepper big data

Machine learning models

Highest Important Variables

AIC

FPE

GCV

HQ

RICE

SCHWARZ

SGMASQ

SHIBATA

Elastic Net

25

70.25421

70.25432

70.26715

72.22405

70.28034

75.73834

69.31760

70.22900

35

62.68245

62.68272

62.70468

65.12901

62.72748

69.55760

61.53141

62.63964

45

62.38588

62.38645

62.42213

65.51397

62.45961

71.25907

60.92971

62.31679

55

62.81465

62.81568

62.86894

66.66956

62.92550

73.85331

61.03908

62.71226

100

61.64484

61.65079

61.82115

68.63553

62.01150

82.54751

58.57586

61.32796

Ridge

25

77.53462

77.53474

77.54891

79.70859

77.56346

83.58707

76.50095

77.50680

35

68.53964

68.53993

68.56394

71.21481

68.58888

76.05722

67.28104

68.49282

45

64.79505

64.79564

64.83270

68.04393

64.87162

74.01089

63.28264

64.72329

55

65.24243

65.24350

65.29882

69.24633

65.35756

76.70773

63.39823

65.13609

100

63.90861

63.91478

64.0914

71.15602

64.28873

85.57889

60.72693

63.58009

LASSO

25

65.14142

65.14153

65.15342

66.96790

65.16565

70.22644

64.27297

65.11805

35

62.45330

62.45357

62.47544

64.89091

62.49816

69.30331

61.30646

62.41064

45

62.34407

62.34463

62.38029

65.47005

62.41774

71.21130

60.88887

62.27502

55

61.92610

61.92712

61.97962

65.72647

62.03538

72.80860

60.17564

61.82516

89

61.85913

61.86510

62.03605

68.87412

62.22706

82.83446

58.77948

61.54114