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Table 3 Leave-one-dataset-out: accuracy criteria (to be maximized) for different numbers v of variables and different values of λ

From: Multiple-input multiple-output causal strategies for gene selection

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
1-RMSE 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
W-L   1-1 5-1 2-0 4-0 5-0 4-0 4-0
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
1-RMSE 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
W-L   3-0 4-0 5-1 3-0 5-0 4-0 6-0
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
1-RMSE 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
W-L   1-0 4-0 6-0 4-0 4-0 5-0 5-1
  1. AUC = Area Under the Curve; 1-RMSE = one minus Root Mean Squared Error; SAR = Squared error, Accuracy, and ROC; F = precision-recall; W-L = 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 (p-value < 0.05 adjusted for multiple testing by Holm's method) and the Wilcoxon paired test on squared errors (p-value < 0.05 adjusted for multiple testing by Holm's method).