parkinsons

Benchmarks

Task Name	Dataset Name	SOTA Result
Regression	parkinsons	NCIW0.25	22
Counterfactual Explanation Generation	Parkinsons	R1	20
Regression	Parkinsons	RMSE0.0397	16
Regression	parkinsons	Interval Score Loss14.486	11
Regression	parkinsons	Quantile Loss0.181	11
Regression	parkinsons	PICP97	11
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
Conditional Density Estimation	Parkinsons 2D (test)	Negative Log-Likelihood0.36	6
Classification	parkinsons UCI KEEL (test)	Accuracy93.1	6
Regression	Parkinsons	Mean MSE78.6647	5
Regression	parkinsons	R2 Score0.9909	4
Classification	parkinsons (80%-20% train-test)	Accuracy100	4
Regression	parkinsons	NCIW8.8	2

Showing 19 of 19 rows