Success Rate Prediction on Experiment 1
2.65R2MLP Neural Net
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| MLP Neural NetHyperparameters=Default, number of parameters=4012026.03 | 2.65 | 28.2 | 34.5 | 38.2 | |
| MLP Neural NetHyperparameters=Bayesian-Optimized, number of parameters=7,9992026.03 | 0.999 | 0.23 | 0.325 | 0.349 | |
| SVRHyperparameters=Bayesian-Optimized, number of parameters=442026.03 | 0.993 | 0.652 | 1.54 | 1.07 | |
| Random ForestHyperparameters=Default, number of parameters=5,7082026.03 | 0.987 | 1.48 | 2.09 | 2.24 | |
| Skewed-Dual.Equation=Equation 112026.03 | 0.95 | 3.23 | 4.05 | 4.85 | |
| Random ForestHyperparameters=Bayesian-Optimized, number of parameters=8,4452026.03 | 0.925 | 3.67 | 4.95 | 5.71 | |
| Dual Gauss.Equation=Equation 42026.03 | 0.816 | 5.44 | 7.75 | 8.11 | |
| Lasso RegressionHyperparameters=Default, number of parameters=32026.03 | 0.743 | 7.36 | 9.17 | 11.3 | |
| Lasso RegressionHyperparameters=Bayesian-Optimized, number of parameters=32026.03 | 0.714 | 7.35 | 9.67 | 11.9 | |
| SVRHyperparameters=Default, number of parameters=452026.03 | 0.213 | 13.8 | 16 | 21.9 |