K-NN Regression on Additive benchmark functions Independent dependence
0.0306f0 Mean ORMSEPD
Evaluation results
| Method | Links | ||||||
|---|---|---|---|---|---|---|---|
| PDnoise fraction=0.3, sample size=500D, hyperparameter tuning=10-fold cross-validation, K-NN weighting scheme=uniform and distance2025.10 | 0.0306 | 0.0637 | 0.2125 | 0.0925 | 0.3578 | 0.2014 | |
| A2D2Enoise fraction=0.3, sample size=500D, hyperparameter tuning=10-fold cross-validation, K-NN weighting scheme=uniform and distance2025.10 | 0.0886 | 0.1336 | 0.3704 | 0.2638 | 0.8381 | 0.2397 | |
| ALEnoise fraction=0.3, sample size=500D, hyperparameter tuning=10-fold cross-validation, K-NN weighting scheme=uniform and distance2025.10 | 0.1093 | 0.1448 | 0.399 | 0.2928 | 1.1314 | 0.2499 | |
| Mnoise fraction=0.3, sample size=500D, hyperparameter tuning=10-fold cross-validation, K-NN weighting scheme=uniform and distance2025.10 | 0.1299 | 0.2021 | 0.3421 | 0.3056 | 1.4528 | 0.235 | |
| DALEnoise fraction=0.3, sample size=500D, hyperparameter tuning=10-fold cross-validation, K-NN weighting scheme=uniform and distance2025.10 | 0.1953 | 0.404 | 0.4835 | 0.5024 | 1.5716 | 0.2666 |