================================================================= hyp_abc_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1564 2 53 136 0 28 128 2 194] [ 0 0 0 0 0 0 0 0 0] [ 915 3 791 403 0 130 417 10 464] [ 5 0 1 407 0 0 1 0 5] [ 0 0 0 0 0 0 0 0 0] [ 0 1 0 0 0 490 1 0 17] [ 10 0 20 17 0 3 645 0 51] [ 0 0 0 0 0 0 0 0 0] [ 164 0 73 32 0 28 66 4 2059]] Scoring Summary: (0) norm = 25.7712% (2) nneo = 74.7526% (3) infl = 2.8640% (5) dcis = 3.7328% (6) indc = 13.5389% (8) bckg = 15.1278% avg lbls = 24.1319% avg bckg = 15.1278% score = 23.2315% <** ================================================================= hyp_abc_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 987 1 47 75 0 15 87 0 129] [ 0 0 0 0 0 0 0 0 0] [ 474 1 396 292 0 66 243 2 342] [ 4 0 3 310 0 0 1 0 2] [ 0 0 0 0 0 0 0 0 0] [ 1 0 6 0 0 261 0 1 27] [ 0 0 5 8 0 0 266 0 23] [ 0 0 0 0 0 0 0 0 0] [ 156 8 56 18 0 23 47 4 1023]] Scoring Summary: (0) norm = 26.3982% (2) nneo = 78.1938% (3) infl = 3.1250% (5) dcis = 11.8243% (6) indc = 11.9205% (8) bckg = 23.3708% avg lbls = 26.2924% avg bckg = 23.3708% score = 26.0002% <** ================================================================= hyp_abc_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[532 0 114 148 0 84 329 0 142] [ 0 0 0 0 0 0 0 0 0] [682 3 172 346 0 207 496 7 300] [ 7 0 0 206 0 4 14 0 0] [ 0 0 0 0 0 0 0 0 0] [ 67 0 29 22 0 19 49 0 57] [ 97 0 74 119 0 55 92 11 16] [ 0 0 0 0 0 0 0 0 0] [112 11 77 6 0 15 33 3 925]] Scoring Summary: (0) norm = 60.5634% (2) nneo = 92.2277% (3) infl = 10.8225% (5) dcis = 92.1811% (6) indc = 80.1724% (8) bckg = 21.7428% avg lbls = 67.1934% avg bckg = 21.7428% score = 62.6484% <** ======================================================================== ================================================================= hyp_nnn_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[2058 0 16 0 0 0 0 0 33] [ 0 0 0 0 0 0 0 0 0] [ 175 0 2822 0 0 13 7 0 116] [ 57 0 48 234 0 20 59 0 1] [ 0 0 0 0 0 0 0 0 0] [ 13 0 1 0 0 490 2 0 3] [ 1 0 3 0 0 0 739 0 3] [ 0 0 0 0 0 0 0 0 0] [ 6 0 24 0 0 1 0 0 2395]] Scoring Summary: (0) norm = 2.3256% (2) nneo = 9.9266% (3) infl = 44.1527% (5) dcis = 3.7328% (6) indc = 0.9383% (8) bckg = 1.2778% avg lbls = 12.2152% avg bckg = 1.2778% score = 11.1215% <** ================================================================= hyp_nnn_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1300 0 10 0 0 0 0 0 31] [ 0 0 0 0 0 0 0 0 0] [ 114 0 1633 0 0 9 2 0 58] [ 46 0 34 184 0 18 38 0 0] [ 0 0 0 0 0 0 0 0 0] [ 5 0 5 0 0 281 0 0 5] [ 1 0 1 1 0 0 299 0 0] [ 0 0 0 0 0 0 0 0 0] [ 1 0 6 1 0 0 0 0 1327]] Scoring Summary: (0) norm = 3.0574% (2) nneo = 10.0771% (3) infl = 42.5000% (5) dcis = 5.0676% (6) indc = 0.9934% (8) bckg = 0.5993% avg lbls = 12.3391% avg bckg = 0.5993% score = 11.1651% <** ================================================================= hyp_nnn_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 828 0 298 1 0 41 78 0 103] [ 0 0 0 0 0 0 0 0 0] [ 886 0 764 12 0 149 194 0 208] [ 41 0 50 97 0 13 29 0 1] [ 0 0 0 0 0 0 0 0 0] [ 64 0 69 0 0 24 35 0 51] [ 86 0 141 7 0 63 156 0 11] [ 0 0 0 0 0 0 0 0 0] [ 37 0 57 0 0 3 13 0 1072]] Scoring Summary: (0) norm = 38.6212% (2) nneo = 65.4767% (3) infl = 58.0087% (5) dcis = 90.1235% (6) indc = 66.3793% (8) bckg = 9.3063% avg lbls = 63.7219% avg bckg = 9.3063% score = 58.2803% <** ========================================================================