================================================================= hyp_gbn_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1127 0 0 17 0 824 126 0 13] [ 0 0 0 0 0 0 0 0 0] [ 0 0 307 118 0 2403 289 0 16] [ 0 0 0 418 0 1 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 509 0 0 0] [ 0 0 0 0 0 20 726 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 0 0 6 0 257 26 0 2137]] Scoring Summary: (0) norm = 46.5116% (2) nneo = 90.2011% (3) infl = 0.2387% (5) dcis = 0.0000% (6) indc = 2.6810% (8) bckg = 11.9126% avg lbls = 27.9265% avg bckg = 11.9126% score = 26.3251% <** ================================================================= hyp_gbn_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 721 0 0 7 0 517 85 0 11] [ 0 0 0 0 0 0 0 0 0] [ 0 0 202 75 0 1398 128 0 13] [ 0 0 0 320 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 296 0 0 0] [ 0 0 0 0 0 15 287 0 0] [ 0 0 0 0 0 0 0 0 0] [ 1 0 0 7 0 160 20 0 1147]] Scoring Summary: (0) norm = 46.2342% (2) nneo = 88.8767% (3) infl = 0.0000% (5) dcis = 0.0000% (6) indc = 4.9669% (8) bckg = 14.0824% avg lbls = 28.0155% avg bckg = 14.0824% score = 26.6222% <** ================================================================= hyp_gbn_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 303 0 29 15 0 805 155 0 42] [ 0 0 0 0 0 0 0 0 0] [ 97 0 59 90 0 1677 260 0 30] [ 1 0 0 207 0 11 12 0 0] [ 0 0 0 0 0 0 0 0 0] [ 1 0 0 3 0 210 23 0 6] [ 1 0 0 37 0 262 163 0 1] [ 0 0 0 0 0 0 0 0 0] [ 3 0 1 0 0 261 20 0 897]] Scoring Summary: (0) norm = 77.5389% (2) nneo = 97.3339% (3) infl = 10.3896% (5) dcis = 13.5802% (6) indc = 64.8707% (8) bckg = 24.1117% avg lbls = 52.7427% avg bckg = 24.1117% score = 49.8796% <** ======================================================================== ================================================================= hyp_mlp_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1556 0 189 55 0 85 142 0 80] [ 0 0 0 0 0 0 0 0 0] [ 591 0 874 248 0 826 454 0 140] [ 0 0 0 417 0 0 2 0 0] [ 0 0 0 0 0 0 0 0 0] [ 1 0 1 3 0 488 14 0 2] [ 1 0 1 43 0 14 686 0 1] [ 0 0 0 0 0 0 0 0 0] [ 42 0 26 8 0 24 80 0 2246]] Scoring Summary: (0) norm = 26.1509% (2) nneo = 72.1034% (3) infl = 0.4773% (5) dcis = 4.1257% (6) indc = 8.0429% (8) bckg = 7.4196% avg lbls = 22.1801% avg bckg = 7.4196% score = 20.7040% <** ================================================================= hyp_mlp_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 999 0 95 38 0 33 96 0 80] [ 0 0 0 0 0 0 0 0 0] [ 449 0 504 137 0 372 242 0 112] [ 0 0 1 315 0 1 3 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 0 3 5 0 283 5 0 0] [ 0 0 0 17 0 12 273 0 0] [ 0 0 0 0 0 0 0 0 0] [ 29 0 16 10 0 7 51 0 1222]] Scoring Summary: (0) norm = 25.5034% (2) nneo = 72.2467% (3) infl = 1.5625% (5) dcis = 4.3919% (6) indc = 9.6026% (8) bckg = 8.4644% avg lbls = 22.6614% avg bckg = 8.4644% score = 21.2417% <** ================================================================= hyp_mlp_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 856 0 156 58 0 65 138 0 76] [ 0 0 0 0 0 0 0 0 0] [ 489 0 503 196 0 555 381 0 89] [ 1 0 0 216 0 4 10 0 0] [ 0 0 0 0 0 0 0 0 0] [ 5 0 23 8 0 163 32 0 12] [ 4 0 12 41 0 164 241 0 2] [ 0 0 0 0 0 0 0 0 0] [ 32 0 27 3 0 17 32 0 1071]] Scoring Summary: (0) norm = 36.5456% (2) nneo = 77.2707% (3) infl = 6.4935% (5) dcis = 32.9218% (6) indc = 48.0603% (8) bckg = 9.3909% avg lbls = 40.2584% avg bckg = 9.3909% score = 37.1716% <** ========================================================================