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ResearchFormerly WizWand

Classification on Vehicle (Accuracy)

85.6AccuracyTabPFN
23.51239.63155.7571.869Mar 18, 2025May 27, 2025Aug 5, 2025Oct 14, 2025Dec 23, 2025Mar 3, 2026May 12, 2026
Updated 5mo ago

Evaluation results

MethodLinks
TabPFNPredictor=TabPFN, Feature Engineering=LLM-FE2025.0385.6
TabPFNPredictor=TabPFN, Feature Engineering=Base2025.0385.2
BrenierIRBase Model=MLP, k=152026.0384.4
IRBase Model=MLP2026.0383.5
UncalibratedBase Model=MLP2026.0382.9
DirBase Model=MLP2026.0382.9
OIBase Model=MLP2026.0382.9
TSBase Model=MLP2026.0382.9
BrenierIRBase Model=MLP, k=502026.0382.9
BinBase Model=MLP2026.0382.4
MSBase Model=MLP2026.0382.4
NS-TDFLOPs=2560, Param=13082025.0681.76
NS-TD2025.0681.76
IRPBase Model=MLP2026.0381.2
MLPFLOPs=896, Param=4762025.0681.06
MLP2025.0681.06
BrenierIRBase Model=MLP, k=302026.0380.9
AutoFeatBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.0378.8
OpenFEBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.0378.5
Logistic RegressionPredictor=Logistic Regression, Feature Engineering=Base2025.0377.2
CAAFEBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.0377.1
Logistic RegressionPredictor=Logistic Regression, Feature Engineering=LLM-FE2025.0376.9
XGBoostPredictor=XGBoost, Feature Engineering=LLM-FE2025.0376.9
LLM-FEBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.0376.9
NS-SRFLOPs=1728, Param=8922025.0676.76
NS-SR2025.0676.76
UncalibratedBase Model=linear SVM2026.0376.5
IRBase Model=linear SVM2026.0376.5
BinBase Model=linear SVM2026.0375.9
KANFLOPs=12138, Param=40322025.0675.76
KAN2025.0675.76
XGBoostPredictor=XGBoost, Feature Engineering=Base2025.0375.4
BaseBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.0375.4
OCTreeBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.0375.3
BrenierIRBase Model=linear SVM, k=152026.0374.9
BrenierIRBase Model=linear SVM, k=502026.0374.7
FeatLLMBackbone=XGBoost, n (number of samples)=846, p (number of features)=182025.0374.4
BrenierIRBase Model=linear SVM, k=302026.0373.3
CatBoostPredictor=CatBoost, Feature Engineering=LLM-FE2025.0372.5
CatBoostPredictor=CatBoost, Feature Engineering=Base2025.0371.9
MLPPredictor=MLP, Feature Engineering=LLM-FE2025.0367.3
FAMeXClassifier=Random Forest, Feature Importance Subset=Top 30%2026.0564.74
FAMeXFeature selection=Top 30%, Classifier=Decision Tree2026.0561.38
FAMeXClassifier=Random Forest, Feature Importance Subset=Bottom 30%2026.0560.29
MSBase Model=linear SVM2026.0358.8
MLPPredictor=MLP, Feature Engineering=Base2025.0358.3
DirBase Model=linear SVM2026.0357.6
FAMeXFeature selection=Bottom 30%, Classifier=Decision Tree2026.0557.33
PFIFeature selection=Bottom 30%, Classifier=Decision Tree2026.0553.91
SHAPFeature selection=Bottom 30%, Classifier=Decision Tree2026.0553.67
SHAPClassifier=Random Forest, Feature Importance Subset=Bottom 30%2026.0553.5
PFIClassifier=Naive Bayes, Feature Importance Subset=Bottom 30%2026.0553.5
FAMeXClassifier=Naive Bayes, Feature Importance Subset=Bottom 30%2026.0545.11
SHAPClassifier=Random Forest, Feature Importance Subset=Top 30%2026.0544.52
PFIClassifier=Random Forest, Feature Importance Subset=Top 30%2026.0544.1
FAMeXClassifier=Naive Bayes, Feature Importance Subset=Top 30%2026.0543.53
PFIFeature selection=Top 30%, Classifier=Decision Tree2026.0543.41
SHAPFeature selection=Top 30%, Classifier=Decision Tree2026.0543.29
OIBase Model=linear SVM2026.0341.2
TSBase Model=linear SVM2026.0341.2
SHAPClassifier=Naive Bayes, Feature Importance Subset=Bottom 30%2026.0538.27
PFIClassifier=Random Forest, Feature Importance Subset=Bottom 30%2026.0538.25
SHAPClassifier=Naive Bayes, Feature Importance Subset=Top 30%2026.0531.13
PFIClassifier=Naive Bayes, Feature Importance Subset=Top 30%2026.0531.09
IRPBase Model=linear SVM2026.0325.9