Table 2 

Holdout: accuracy criteria (to be maximized) for different numbers v of variables and different values of λ 

v = 20 
λ = 0 
λ = 0.2 
λ = 0.4 
λ = 0.6 
λ = 0.8 
λ = 0.9 
λ = 1 
λ = 2 


AUC 
0.688 
0.688 
0.694 
0.699 
0.703 
0.704 
0.705 
0.707 
1RMSE 
0.460 
0.466 
0.481 
0.493 
0.504 
0.510 
0.515 
0.542 
SAR 
0.559 
0.561 
0.569 
0.575 
0.580 
0.583 
0.585 
0.595 
F 
0.255 
0.254 
0.260 
0.262 
0.265 
0.265 
0.266 
0.274 


WL 
10 
30 
50 
60 
50 
50 
50 





v = 50 
λ = 0 
λ = 0.2 
λ = 0.4 
λ = 0.6 
λ = 0.8 
λ = 0.9 
λ = 1 
λ = 2 


AUC 
0.693 
0.698 
0.702 
0.706 
0.709 
0.710 
0.711 
0.715 
1RMSE 
0.451 
0.458 
0.465 
0.471 
0.477 
0.479 
0.482 
0.503 
SAR 
0.552 
0.556 
0.562 
0.567 
0.571 
0.572 
0.574 
0.583 
F 
0.263 
0.265 
0.268 
0.270 
0.272 
0.271 
0.273 
0.277 


WL 
20 
30 
30 
20 
20 
30 
40 





v = 100 
λ = 0 
λ = 0.2 
λ = 0.4 
λ = 0.6 
λ = 0.8 
λ = 0.9 
λ = 1 
λ = 2 


AUC 
0.699 
0.704 
0.708 
0.711 
0.714 
0.715 
0.715 
0.716 
1RMSE 
0.454 
0.457 
0.459 
0.463 
0.467 
0.470 
0.472 
0.487 
SAR 
0.545 
0.549 
0.553 
0.557 
0.561 
0.563 
0.564 
0.573 
F 
0.272 
0.271 
0.272 
0.274 
0.274 
0.274 
0.275 
0.284 


WL 
10 
10 
10 
20 
30 
41 
41 



AUC = Area Under the Curve; 1RMSE = one minus Root Mean Squared Error; SAR = Squared error, Accuracy, and ROC; F = precisionrecall; WL = Win Loss reporting the number of datasets for which the causal filter is significantly more (W) or less (L) accurate than the conventional ranking filter according both to the McNemar test (pvalue < 0.05 adjusted for multiple testing by Holm's method) and the Wilcoxon paired test on squared errors (pvalue < 0.05 adjusted for multiple testing by Holm's method). 

Bontempi et al. BMC Bioinformatics 2011 12:458 doi:10.1186/1471210512458 