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Regression on Wine Quality

0.2058RMSEXGB
0.1828320.3378660.49290.647934May 8, 2026May 11, 2026May 15, 2026May 19, 2026May 23, 2026May 27, 2026May 31, 2026

Evaluation results

MethodLinks
XGBConfiguration=Pipeline2026.050.2058————
RidgeConfiguration=Pipeline2026.050.2084————
SVRConfiguration=Pipeline2026.050.2085————
LRConfiguration=Pipeline2026.050.2093————
MLPConfiguration=Pipeline2026.050.2148————
KNNConfiguration=Pipeline2026.050.2164————
XGBConfiguration=Single2026.050.2264————
SVRConfiguration=Single2026.050.2268————
MLPConfiguration=Single2026.050.2288————
KNNConfiguration=Single2026.050.2366————
RidgeConfiguration=Single2026.050.2463————
LRConfiguration=Single2026.050.2465————
DTSemNet Top-kHeight=5, Number of features (Nf)=11, Number of training samples (Ns)=45472026.050.69————
DTSemNet STEHeight=5, Number of features (Nf)=11, Number of training samples (Ns)=45472026.050.7————
DGT-LinearHeight=5, Number of features (Nf)=11, Number of training samples (Ns)=45472026.050.7————
CARTNumber of features (Nf)=11, Number of training samples (Ns)=45472026.050.78————
ADAModel=XGB2026.06————0
ADAModel=MLP2026.06————23.6
C-MixupModel=XGB2026.06————-2
C-MixupModel=MLP2026.06————13.1
CRDAModel=XGB2026.06————-2.8
CRDAModel=MLP2026.06————-2.9
CTGANModel=XGB2026.06————0.3
CTGANModel=MLP2026.06————2.1
Offline Random Forest Regression (RFR)Training mode=Offline, Access=Unlimited access to dataset2026.02—0.3450.555——
Prototype-based generative replay frameworkConfiguration=Best config, Learning mode=Incremental/Online2026.02—0.5290.319——
TabDDPMModel=XGB2026.06————-2.6
TabDDPMModel=MLP2026.06————-5.5
TVAEModel=XGB2026.06————-0.5
TVAEModel=MLP2026.06————-0.8