Success Rate and Distribution Parameter Regression on Experiment 2
0.999R2MLP Neural Net
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| MLP Neural NetFormula=SRy, Number of parameters=21,058, Hyperparameter optimization=Bayesian-optimized2026.03 | 0.999 | 0.18 | 0.307 | 0.295 | |
| Random ForestFormula=SRy, Number of parameters=5,528, Hyperparameter optimization=default2026.03 | 0.967 | 2.24 | 3.81 | 3.27 | |
| Random ForestFormula=SRy, Number of parameters=8,171, Hyperparameter optimization=Bayesian-optimized2026.03 | 0.967 | 2.54 | 3.8 | 3.81 | |
| Skewed-Dual (SRy)Formula=Equation 112026.03 | 0.953 | 3.13 | 4.56 | 5.24 | |
| Skewed-Dual (gamma_1y)Formula=Equation 16 using Dedge y, Regression Constants=cy = 1.20, dy = −0.1992026.03 | 0.873 | 0.0915 | 0.12 | 81.6 | |
| Skewed-Dual (sigma_y)Formula=Equation 17 using H & Marginy, Regression Constants=ey = 0.123, fy = 0.0371, gy = 0.415, hy = 1.31, iy = 0.01302026.03 | 0.871 | 0.0675 | 0.0915 | 5.61 | |
| SVRFormula=SRy, Number of parameters=44, Hyperparameter optimization=Bayesian-optimized2026.03 | 0.835 | 3.7 | 8.56 | 4.72 | |
| Dual Gauss. (SRy)Formula=Equation 42026.03 | 0.699 | 7.51 | 11.6 | 12.5 | |
| Skewed-Dual (mu_y)Formula=Equation 18 using Dedge y, Regression Constants=jy = 0.804, ky = −0.0961, ly = 3.602026.03 | 0.641 | 0.124 | 0.167 | 36 | |
| Lasso RegressionFormula=SRy, Number of parameters=3, Hyperparameter optimization=default2026.03 | 0.608 | 10.5 | 13.2 | 16.7 | |
| Lasso RegressionFormula=SRy, Number of parameters=3, Hyperparameter optimization=Bayesian-optimized2026.03 | 0.597 | 10.3 | 13.4 | 17.1 | |
| Dual Gauss. (sigma_y^2)Formula=Equation 1, Regression Constants=ay = 1.23, by = 0.01642026.03 | 0.37 | 0.355 | 0.459 | 39.1 | |
| SVRFormula=SRy, Number of parameters=46, Hyperparameter optimization=default2026.03 | 0.106 | 16.2 | 19.9 | 28.9 | |
| MLP Neural NetFormula=SRy, Number of parameters=401, Hyperparameter optimization=default2026.03 | -2.49 | 32.1 | 39.4 | 41.4 |