A. Average discrepancies between Diagnoses All PC |  |  | |||
---|---|---|---|---|---|
Model | Target | Data | % Discrepancies | Â | Â |
Multinomial logistic regression | Diagnoses | ALL PC | 37.3 | Â | |
Boot strap forest | Diagnoses | ALL PC | 24.1 | Â | |
Partial least squares | Diagnoses | ALL PC | 37.3 | Â |
B. Average discrepancies between Clusters All PC |  |  | |||
---|---|---|---|---|---|
Model | Target | Data | % Discrepancies | Â | Â |
Multinomial logistic regression | Clusters | ALL PC | 10.0 | Â | |
Boot strap forest | Clusters | ALL PC | 12.2 | Â | |
Partial least squares | Clusters | ALL PC | 15.1 | Â |
C. Average discrepancies CPPC, UPC, & PBPC |  |  | |||
---|---|---|---|---|---|
Model | Target | Data | % Discrepancies | Â | Â |
Multinomial logistic regression | Diagnoses | CPPC | 36.9 | Â | |
Boot strap forest | Diagnoses | CPPC | 25.6 | Â | |
Partial least squares | Diagnoses | CPPC | 27.2 | Â | |
Multinomial logistic regression | Diagnoses | UPC | 24.4 | Â | |
Boot strap forest | Diagnoses | UPC | 24.9 | Â | |
Partial least squares | Diagnoses | UPC | 27.0 | Â | |
Multinomial logistic regression | Diagnoses | PBPC | 35.2 | Â | |
Boot strap forest | Diagnoses | PBPC | 27.5 | Â | |
Partial least squares | Diagnoses | PBPC | 29.2 | Â |
D. Average per sample discrepancies between Diagnoses All PC |  |  | |||
---|---|---|---|---|---|
Model | Target | Data | % Discrepancies | Â | Â |
Multinomial logistic regression vs Boot strap forest | Diagnoses | ALL PC | 33.1 | Â | |
Multinomial logistic regression vs Partial least squares | Diagnoses | ALL PC | 32.7 | Â | |
Boot strap forest vs Partial least squares | Diagnoses | ALL PC | 33.1 | Â |
E. Average per sample discrepancies between Clusters All PC |  |  | |||
---|---|---|---|---|---|
Model | Target | Data | % Discrepancies | Â | Â |
Multinomial logistic regression vs Boot strap forest | Clusters | ALL PC | 12.4 | Â | |
Multinomial logistic regression vs Partial least squares | Clusters | ALL PC | 13.5 | Â | |
Boot strap forest vs Partial least squares | Clusters | ALL PC | 14.4 | Â |
F. Additional models for diagnoses all PC, CPPC, PBPC, & UPC* | |||||
---|---|---|---|---|---|
Model | Target | Data | Mean accuracy | Average precision/recall | Percent errors |
Extreme Gradient Boosting | Diagnoses | ALL PC | 0.74 | 0.79 | 36.5 |
Linear Discriminant Analysis | Diagnoses | CPPC | 0.75 | 0.78 | 32.6 |
Extra Tree Classifier | Diagnoses | PBPC | 0.75 | 0.76 | 35.3 |
Linear discriminant analysis | Diagnoses | UPC | 0.76 | 0.70 | 33.9 |
G. Additional models for clusters all PC, CPPC, PBPC, & UPC* | |||||
---|---|---|---|---|---|
Model | Target | Data | Mean accuracy | Average precision/recall | Percent errors |
Multinomial logistic regression | Clusters | ALL PC | 0.93 | 0.95 | 6.9 |
Extra tree classifier | Clusters | CPPC | 0.90 | 0.93 | 6.9 |
Extreme gradient boosting | Clusters | PBPC | 0.92 | 0.94 | 6.2 |
Multinomial logistic regression | Clusters | UPC | 0.92 | 0.94 | 6.9 |