Data

Model

Normalization

None

Z-standard

Min-Max

MaxAbs (−1, 1)

Quantile Transform

Quantile Normalize

Categorical - Continuous Target

Poisson Regression

Type of Loss

MSE

MSE

MSE

MSE

MSE

MSE

Total Loss

1.929

631.554

2.208

2.070

6.928

1.929

Bias

1.743

6.792

1.801

1.928

1.271

1.740

Variance

0.186

624.762

0.407

0.407

5.657

0.188

Noise

0.000

0.000

0.000

0.264

0.000

0.000

Variance-Bias Ratio

0.107

91.989

0.226

0.211

4.452

0.108

Percent Change from Raw

~

32,734.401

114.423

107.289

359.076

99.957

Decision Tree

Total Loss

2.087

2.127

2.137

2.140

2.137

2.119

Bias

1.123

1.199

1.213

1.212

1.213

1.264

Variance

0.964

0.927

0.924

0.924

0.924

0.855

Noise

0.000

0.000

0.000

0.004

0.000

0.000

Variance-Bias Ratio

0.859

0.773

0.762

0.762

0.762

0.677

Percent Change from Raw

~

101.899

102.382

102.521

102.382

101.557

Random Forest

Total Loss

1.820

2.129

2.113

2.113

2.113

1.821

Bias

1.608

2.029

1.997

1.997

1.997

1.609

Variance

0.212

0.100

0.116

0.116

0.116

0.212

Noise

0.000

0.000

0.000

0.000

0.000

0.000

Variance-Bias Ratio

0.132

0.049

0.058

0.058

0.058

0.132

Percent Change from Raw

~

116.956

116.100

116.100

116.100

100.040

SVM

Total Loss

1.940

2.029

2.201

2.247

2.188

1.938

Bias

1.775

1.865

2.157

2.244

2.133

1.771

Variance

0.165

0.164

0.044

0.044

0.055

0.166

Noise

0.000

0.000

0.000

0.041

0.000

0.000

Variance-Bias Ratio

0.093

0.088

0.020

0.020

0.026

0.094

Percent Change from Raw

~

104.577

113.429

115.807

112.765

99.867

Gradient Boosting

Total Loss

1.746

2.042

2.000

2.001

2.000

1.746

Bias

1.531

1.876

1.787

1.787

1.787

1.530

Variance

0.216

0.166

0.213

0.213

0.213

0.216

Noise

0.000

0.000

0.000

0.000

0.000

0.000

Variance-Bias Ratio

0.141

0.089

0.119

0.119

0.119

0.141