Policy Decision Making on Synthetic (d_phi=1) out-sample
8.18Error Rate (ER_out)k-NN
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| k-NNNumber of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 8.18 | — | |
| C-ForestNumber of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 16.1 | — | |
| BARTNumber of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 17.37 | — | |
| InvTARNetRepresentation dimension (d_phi)=1, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 29.51 | -0.95 | |
| TARNetRepresentation dimension (d_phi)=1, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 30.79 | -12.89 | |
| RCFR (WM; α = 1.0)Representation dimension (d_phi)=1, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 33.02 | -3.58 | |
| BNN (MMD; α = 0.1)Representation dimension (d_phi)=1, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 34.32 | -15.41 | |
| CFR (WM; α = 1.0)Representation dimension (d_phi)=1, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 34.97 | -14.27 | |
| BWCFR (WM; α = 1.0)Representation dimension (d_phi)=1, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 34.97 | -10.02 | |
| CFR-ISW (WM; α = 1.0)Representation dimension (d_phi)=1, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 35 | -9.43 | |
| CFR (MMD; α = 0.1)Representation dimension (d_phi)=1, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 35.01 | -14.27 | |
| CFR (WM; α = 2.0)Representation dimension (d_phi)=1, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 35.18 | -13.63 | |
| CFR (MMD; α = 0.5)Representation dimension (d_phi)=1, Number of training samples (ntrain)=1,000, Delta parameter (δ)=0.00052023.11 | 35.79 | -11.43 |