Regression on DS4
0.9297R-SquaredBatch Regression
Evaluation results
| Method | Links | |
|---|---|---|
| Batch RegressionAlgorithm=Pseudo-Inverse2025.12 | 0.9297 | |
| MBGDLearning rate (eta)=0.001, Mini-batch size (K)=M * 5, Epochs (E)=NK * 102025.12 | 0.9296 | |
| OLR-WABase weight (Wbase)=0.5, Weight increment (Winc)=0.5, BK=N * 0.1, Mini-batch size (K)=M * 52025.12 | 0.9246 | |
| LMSLearning rate (eta)=0.012025.12 | 0.9072 | |
| SGDLearning rate (eta)=0.01, Epochs (E)=N * 22025.12 | 0.9066 | |
| PAVariant=PA-III, Cost parameter (C)=0.01, Sensitivity parameter (epsilon)=0.012025.12 | 0.8726 | |
| OLRLearning rate (eta)=0.001, Epochs (E)=N * 2, Regularization parameter (lambda)=0.12025.12 | 0.8641 | |
| ORRLearning rate (eta)=0.001, Epochs (E)=N * 2, Regularization parameter (lambda)=0.12025.12 | 0.8554 | |
| RLSRegularization parameter (lambda)=0.99, Delta (delta)=0.012025.12 | 0.6267 |