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Downstream Performance Prediction on CH (Chess) corrupted with Laplace noise

-0.612Downstream Performance

Univariate + MLP

Updated 4d ago

Evaluation Results

Method	Links
Univariate + MLP 2023.11		-0.612	7.64
No FS + MLP 2023.11		-0.504	7.73
No FS + MLP 2023.11		-0.471	8.27
LassoNet + MLP 2023.11		-0.46	5.09
Lasso + MLP 2023.11		-0.456	6.55
XGBoost + MLP 2023.11		-0.454	2.64
XGBoost + MLP 2023.11		-0.454	2
AM + MLP 2023.11		-0.453	3.91
LassoNet + MLP 2023.11		-0.451	4.45
RF + MLP 2023.11		-0.45	4.45
RF + MLP 2023.11		-0.449	5
Univariate + MLP 2023.11		-0.448	7
Lasso + MLP 2023.11		-0.447	4.36
1L Lasso + MLP 2023.11		-0.447	3.73
1L Lasso + MLP 2023.11		-0.445	3.09
AGL + MLP 2023.11		-0.443	3.36
AGL + MLP 2023.11		-0.443	3.09
Deep Lasso + MLP 2023.11		-0.443	2
Deep Lasso + MLP 2023.11		-0.441	1.91