================================================================= hyp_rnf_train.csv : **> Error: lists are not the same length (10066)(6285) Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[401 238 81 258 43 416 0 0 0] [ 0 0 0 0 0 0 0 0 0] [425 331 122 245 90 512 0 0 0] [ 66 30 40 34 8 60 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 91 44 30 86 14 104 0 0 0] [144 100 19 64 62 169 0 0 0] [ 0 0 0 0 0 0 0 0 0] [387 279 138 214 51 368 0 0 0]] Scoring Summary: (0) norm = 72.0946% (2) nneo = 92.9275% (3) infl = 85.7143% (5) dcis = 71.8157% (6) indc = 100.0000% (8) bckg = 100.0000% avg lbls = 84.5104% avg bckg = 100.0000% score = 86.0594% <** ================================================================= hyp_rnf_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 905 192 39 43 1 161 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 671 376 142 332 2 293 0 0 0] [ 22 12 250 34 2 0 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 59 72 11 136 1 17 0 0 0] [ 80 75 56 78 2 11 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 105 80 10 28 0 1112 0 0 0]] Scoring Summary: (0) norm = 32.5130% (2) nneo = 92.1806% (3) infl = 89.3750% (5) dcis = 94.2568% (6) indc = 100.0000% (8) bckg = 100.0000% avg lbls = 81.6651% avg bckg = 100.0000% score = 83.4986% <** ================================================================= hyp_rnf_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 838 255 47 64 2 143 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 796 574 147 464 6 226 0 0 0] [ 11 7 203 9 1 0 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 85 67 10 53 0 28 0 0 0] [ 62 80 52 262 3 5 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 65 44 3 26 0 1044 0 0 0]] Scoring Summary: (0) norm = 37.8799% (2) nneo = 93.3574% (3) infl = 96.1039% (5) dcis = 88.4774% (6) indc = 100.0000% (8) bckg = 100.0000% avg lbls = 83.1637% avg bckg = 100.0000% score = 84.8473% <** ======================================================================== ================================================================= hyp_cnn_train.csv : **> Error: lists are not the same length (10066)(6285) Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[329 60 386 42 1 139 48 48 384] [ 0 0 0 0 0 0 0 0 0] [295 91 581 72 4 87 91 48 456] [ 38 9 64 31 0 25 8 6 57] [ 0 0 0 0 0 0 0 0 0] [ 77 4 84 18 2 62 14 9 99] [ 88 27 164 9 2 18 62 27 161] [ 0 0 0 0 0 0 0 0 0] [264 60 480 92 0 68 51 70 352]] Scoring Summary: (0) norm = 77.1051% (2) nneo = 66.3188% (3) infl = 86.9748% (5) dcis = 83.1978% (6) indc = 88.8889% (8) bckg = 75.5045% avg lbls = 80.4971% avg bckg = 75.5045% score = 79.9978% <** ================================================================= hyp_cnn_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 497 11 585 12 1 90 26 20 99] [ 0 0 0 0 0 0 0 0 0] [ 234 18 959 54 1 176 71 58 245] [ 3 0 51 232 0 23 5 6 0] [ 0 0 0 0 0 0 0 0 0] [ 12 2 165 10 0 60 27 7 13] [ 16 1 136 27 0 48 33 17 24] [ 0 0 0 0 0 0 0 0 0] [ 28 20 94 1 0 5 4 9 1174]] Scoring Summary: (0) norm = 62.9381% (2) nneo = 47.1916% (3) infl = 27.5000% (5) dcis = 79.7297% (6) indc = 89.0728% (8) bckg = 12.0599% avg lbls = 61.2865% avg bckg = 12.0599% score = 56.3638% <** ================================================================= hyp_cnn_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 384 7 695 17 1 106 31 37 71] [ 0 0 0 0 0 0 0 0 0] [ 286 8 1189 67 3 271 70 84 235] [ 1 0 22 188 0 12 5 3 0] [ 0 0 0 0 0 0 0 0 0] [ 34 2 98 4 0 34 12 7 52] [ 22 0 233 38 0 81 59 20 11] [ 0 0 0 0 0 0 0 0 0] [ 22 28 67 0 0 6 5 11 1043]] Scoring Summary: (0) norm = 71.5345% (2) nneo = 46.2720% (3) infl = 18.6147% (5) dcis = 86.0082% (6) indc = 87.2845% (8) bckg = 11.7597% avg lbls = 61.9428% avg bckg = 11.7597% score = 56.9245% <** ========================================================================