Data

Model

Normalization

None

Z-standard

Min-Max

MaxAbs (−1, 1)

Quantile Transform

Quantile Normalize

Categorical - Continuous Target

Linear

Type of Loss

MSE

MSE

MSE

MSE

MSE

MSE

Total Loss

0.243

3,414,987,786,139,060,000.000

0.243

0.243

12,972,148,828.537

0.243

Bias

0.241

2,518,113,273,062,070,000.000

0.241

0.241

27,556,466.022

0.241

Variance

0.003

896,874,513,076,987,000.000

0.003

0.003

12,944,592,362.515

0.003

Noise

0.000

3584.000

0.000

0.000

0.000

0.000

Variance-Bias Ratio

0.011

0.356

0.011

0.011

469.748

0.011

Percent Change from Raw

~

1,403,381,296,822,710,000,000.000

100.000

100.000

5,330,874,423,463.480

100.020

Decision Tree

Total Loss

0.245

1.325

0.243

0.243

0.243

0.243

Bias

0.239

1.198

0.240

0.240

0.240

0.240

Variance

0.006

0.126

0.003

0.003

0.003

0.003

Noise

0.000

0.000

0.000

0.000

0.000

0.000

Variance-Bias Ratio

0.025

0.105

0.013

0.013

0.013

0.013

Percent Change from Raw

~

540.783

99.133

99.133

99.133

99.133

Random Forest

Total Loss

0.364

1.605

0.364

0.364

0.364

0.364

Bias

0.350

1.489

0.350

0.350

0.350

0.350

Variance

0.013

0.116

0.013

0.013

0.013

0.013

Noise

0.000

0.000

0.000

0.000

0.000

0.000

Variance-Bias Ratio

0.038

0.078

0.038

0.038

0.038

0.038

Percent Change from Raw

~

441.086

100.000

100.000

100.000

100.000

SVM

Total Loss

0.242

0.689

0.242

0.242

0.242

0.242

Bias

0.238

0.689

0.238

0.238

0.238

0.238

Variance

0.004

0.000

0.004

0.004

0.004

0.004

Noise

0.000

0.000

0.000

0.000

0.000

0.000