Yacht
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Regression | Yacht | RMSE0.65 | 49 | |
| Regression | Yacht UCI (test) | RMSE0.69 | 20 | |
| Data Imputation | Yacht | MAE0.1478 | 14 | |
| Regression | Yacht | Negative Log Predictive Density4.6 | 12 | |
| Missing data imputation | Yacht 30% MCAR | Avg Error0.151 | 11 | |
| Regression | Yacht OOD rescaled by 0.1 non-normalized UCI (test) | RMSE14.34 | 8 | |
| Regression | Yacht | Relative Percentage Error3 | 8 | |
| Regression | Yacht | Avg NLL Relative %0.14 | 8 | |
| Bayesian Neural Network Inference | Yacht (UCI) (test) | Test Log-Likelihood-0.73 | 8 | |
| Regression | Yacht UCI rescaled 3x std normalized (test) | RMSE15.67 | 7 | |
| Regression | Yacht | NLL0.16 | 6 | |
| Regression | Yacht UCI (20% test) | RMSE0.353 | 6 | |
| Predictive Density Estimation | Yacht Laplace noise, 15% Corruptions | Negative Log Predictive Density2.27 | 6 | |
| Predictive Density Estimation | Yacht Student-t noise, 15% Corruptions | Negative Log Predictive Density2.42 | 6 | |
| Predictive Density Estimation | Yacht Constant noise, 15% Corruptions | Neg Log Pred Density1.79 | 6 | |
| Predictive Density Estimation | Yacht Uniform noise, 15% Corruptions | Negative Log Density2.37 | 6 | |
| Heteroskedastic Regression | Yacht (test) | µ MSE0.0077 | 5 | |
| Out-of-distribution detection | Yacht (UCI) (test) | OOD Detection Accuracy97.3 | 5 | |
| Regression | Yacht | QICE5.01 | 5 | |
| Regression | Yacht Focused Outliers | MAE0.0159 | 5 | |
| Regression | Yacht Asymmetric Outliers | MAE0.027 | 5 | |
| Regression | Yacht Uniform Outliers | MAE0.12 | 5 | |
| Regression | Yacht No Outliers | MAE0.0142 | 5 | |
| Regression | Yacht Focused Outliers (test) | NLPD-2.63 | 5 | |
| Regression | Yacht (test) | Median MSE1.047 | 4 |
Showing 25 of 33 rows