parkinsons

Benchmarks

Task Name	Dataset Name	SOTA Result
Regression	parkinsons	NCIW0.25	22
Counterfactual Explanation Generation	Parkinsons	R1	20
Regression	Parkinsons	RMSE0.0397	16
Classification	parkinsons UCI KEEL (test)	Accuracy93.1	12
Regression	parkinsons	Interval Score Loss14.486	11
Regression	parkinsons	Quantile Loss0.181	11
Regression	parkinsons	PICP97	11
Classification	Parkinsons 80% (train)	VI Score82	8
Classification	Parkinsons UCI (test 20%)	VI9.06	8
Denoising	Parkinsons	Improvement (%)82.8	8
Downstream ML Utility	Parkinsons	F1-score96.6	8
Tabular Synthetic Data Generation	Parkinsons	KS Statistic0.034	8
Active Learning	Parkinsons	AULC0.629	8
Classification	Parkinsons	ROC AUC0.958	8
Tabular Classification	Parkinsons	Cohen's Kappa0.772	8
Tabular Data Generation	Parkinsons	Peak GPU Memory Usage (GB)2.1	7
Regression	Parkinsons Total (5-fold CV)	RMSE1.86	7
Classification	UCI Parkinsons (train)	Accuracy94.49	6
Conditional Density Estimation	Parkinsons 2D (test)	Negative Log-Likelihood0.36	6
Regression	Parkinsons	Mean MSE78.6647	5
Regression	parkinsons	R2 Score0.9909	4
Classification	parkinsons (80%-20% train-test)	Accuracy100	4
Classification	Parkinsons (5-fold CV)	Accuracy94.9	2
Model Stealing	Parkinsons	Number of Queries296	2
Regression	parkinsons	NCIW8.8	2

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