Data	Model	Normalization	None	Z-standard	Min-Max	MaxAbs (−1, 1)	Quantile Transform	Quantile Normalize
Categorical - Binary Target	Logistic	Total Loss	0.473	0.476	0.473	0.473	0.473	0.473
		Bias	0.281	0.266	0.281	0.281	0.281	0.281
		Variance	0.192	0.209	0.192	0.282	0.192	0.192
		Noise	0.000	0.000	0.000	0.090	0.000	0.000
		Variance-Bias Ratio	0.683	0.786	0.683	1.004	0.683	0.683
		Percent Change from Raw	~	100.573	100.000	100.000	100.000	100.000
	Decision Tree
		Total Loss	0.502	0.480	0.485	0.485	0.485	0.485
		Bias	0.338	0.281	0.306	0.306	0.306	0.306
		Variance	0.164	0.200	0.178	0.284	0.178	0.178
		Noise	0.000	0.000	0.000	0.105	0.000	0.000
		Variance-Bias Ratio	0.484	0.710	0.582	0.926	0.582	0.582
		Percent Change from Raw	~	95.636	96.508	96.508	96.508	96.508
	Random Forest	Total Loss	0.476	0.484	0.476	0.476	0.476	0.476
		Bias	0.288	0.282	0.288	0.288	0.288	0.288
		Variance	0.187	0.201	0.187	0.272	0.187	0.187
		Noise	0.000	0.000	0.000	0.085	0.000	0.000
		Variance-Bias Ratio	0.649	0.714	0.649	0.944	0.649	0.649
		Percent Change from Raw	~	101.709	100.000	100.000	100.000	100.000
	SVM	Total Loss	0.476	0.473	0.476	0.476	0.476	0.476
		Bias	0.288	0.285	0.288	0.288	0.288	0.288
		Variance	0.188	0.187	0.188	0.275	0.188	0.188
		Noise	0.000	0.000	0.000	0.087	0.000	0.000
		Variance-Bias Ratio	0.653	0.656	0.653	0.957	0.653	0.653
		Percent Change from Raw	~	99.398	100.000	100.000	100.000	100.000
	Gradient Boosting	Total Loss	0.483	0.496	0.483	0.483	0.483	0.483
		Bias	0.305	0.326	0.305	0.305	0.305	0.305
		Variance	0.178	0.170	0.178	0.284	0.178	0.178
		Noise	0.000	0.000	0.000	0.106	0.000	0.000
		Variance-Bias Ratio	0.585	0.521	0.585	0.932	0.585	0.585
		Percent Change from Raw	~	102.711	100.000	100.000	100.000	100.000
	Neural Network	Total Loss	0.491	0.496	0.491	0.491	0.491	0.490