Table 3 

Leaveonedatasetout: 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 
0.678 
0.674 
0.678 
0.680 
0.682 
0.682 
0.680 
0.669 
1RMSE 
0.447 
0.448 
0.467 
0.469 
0.482 
0.528 
0.544 
0.556 
SAR 
0.553 
0.552 
0.560 
0.561 
0.566 
0.582 
0.586 
0.586 
F 
0.280 
0.275 
0.275 
0.281 
0.279 
0.283 
0.287 
0.276 


WL 
11 
51 
20 
40 
50 
40 
40 





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


AUC 
0.681 
0.687 
0.692 
0.693 
0.698 
0.700 
0.700 
0.693 
1RMSE 
0.428 
0.438 
0.453 
0.457 
0.464 
0.473 
0.490 
0.516 
SAR 
0.542 
0.551 
0.559 
0.561 
0.565 
0.569 
0.576 
0.582 
F 
0.284 
0.284 
0.281 
0.281 
0.285 
0.291 
0.298 
0.303 


WL 
30 
40 
51 
30 
50 
40 
60 





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


AUC 
0.687 
0.694 
0.704 
0.708 
0.711 
0.706 
0.708 
0.676 
1RMSE 
0.430 
0.436 
0.449 
0.457 
0.463 
0.463 
0.476 
0.477 
SAR 
0.537 
0.545 
0.556 
0.562 
0.566 
0.565 
0.571 
0.561 
F 
0.290 
0.292 
0.294 
0.296 
0.299 
0.294 
0.304 
0.288 


WL 
10 
40 
60 
40 
40 
50 
51 



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 