================================================================= hyp_rnf_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[2073 0 6 0 0 1 0 1 26] [ 0 0 0 0 0 0 0 0 0] [ 3 1 3074 0 0 0 0 1 54] [ 2 0 8 408 0 0 1 0 0] [ 0 0 0 0 0 0 0 0 0] [ 4 0 18 0 0 482 2 0 3] [ 5 0 10 1 0 0 726 0 4] [ 0 0 0 0 0 0 0 0 0] [ 2 1 6 0 0 0 0 0 2417]] Scoring Summary: (0) norm = 1.6137% (2) nneo = 1.8832% (3) infl = 2.6253% (5) dcis = 5.3045% (6) indc = 2.6810% (8) bckg = 0.3710% avg lbls = 2.8215% avg bckg = 0.3710% score = 2.5765% <** ================================================================= 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: [[ 657 0 548 2 0 1 11 0 122] [ 0 0 0 0 0 0 0 0 0] [ 318 0 1291 3 0 22 16 8 158] [ 10 0 204 73 0 19 4 9 1] [ 0 0 0 0 0 0 0 0 0] [ 18 1 247 7 0 15 0 4 4] [ 39 1 194 3 0 6 49 3 7] [ 0 0 0 0 0 0 0 0 0] [ 67 6 76 0 0 0 10 2 1174]] Scoring Summary: (0) norm = 51.0067% (2) nneo = 28.9097% (3) infl = 77.1875% (5) dcis = 94.9324% (6) indc = 83.7748% (8) bckg = 12.0599% avg lbls = 67.1622% avg bckg = 12.0599% score = 61.6520% <** ================================================================= 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: [[ 616 2 630 0 0 0 11 2 88] [ 0 0 0 0 0 0 0 0 0] [ 409 2 1619 2 0 24 19 9 129] [ 10 3 146 49 0 15 2 6 0] [ 0 0 0 0 0 0 0 0 0] [ 37 1 164 2 0 1 19 0 19] [ 32 0 339 7 0 36 21 26 3] [ 0 0 0 0 0 0 0 0 0] [ 54 4 52 0 0 0 6 3 1063]] Scoring Summary: (0) norm = 54.3365% (2) nneo = 26.8414% (3) infl = 78.7879% (5) dcis = 99.5885% (6) indc = 95.4741% (8) bckg = 10.0677% avg lbls = 71.0057% avg bckg = 10.0677% score = 64.9119% <** ======================================================================== ================================================================= hyp_ffn_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[2075 15 9 0 0 0 0 1 7] [ 0 0 0 0 0 0 0 0 0] [ 10 38 3072 0 0 0 0 0 13] [ 0 1 0 418 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 1 1 0 0 507 0 0 0] [ 0 2 0 0 0 0 744 0 0] [ 0 0 0 0 0 0 0 0 0] [ 146 244 279 1 0 6 5 17 1728]] Scoring Summary: (0) norm = 1.5187% (2) nneo = 1.9470% (3) infl = 0.2387% (5) dcis = 0.3929% (6) indc = 0.2681% (8) bckg = 28.7716% avg lbls = 0.8731% avg bckg = 28.7716% score = 3.6629% <** ================================================================= hyp_ffn_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[440 30 391 48 7 110 144 90 81] [ 0 0 0 0 0 0 0 0 0] [392 90 744 81 18 145 172 83 91] [ 58 9 124 35 8 38 36 12 0] [ 0 0 0 0 0 0 0 0 0] [ 72 5 121 23 5 28 26 14 2] [ 68 15 106 17 2 25 32 25 12] [ 0 0 0 0 0 0 0 0 0] [202 118 187 12 2 63 88 63 600]] Scoring Summary: (0) norm = 67.1887% (2) nneo = 59.0308% (3) infl = 89.0625% (5) dcis = 90.5405% (6) indc = 89.4040% (8) bckg = 55.0562% avg lbls = 79.0453% avg bckg = 55.0562% score = 76.6464% <** ================================================================= hyp_ffn_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[405 50 442 56 13 107 136 84 56] [ 0 0 0 0 0 0 0 0 0] [508 86 878 109 26 198 227 113 68] [ 43 3 76 33 1 29 34 11 1] [ 0 0 0 0 0 0 0 0 0] [ 46 11 108 9 3 20 16 11 19] [ 91 8 182 36 9 61 49 21 7] [ 0 0 0 0 0 0 0 0 0] [168 169 185 8 0 34 72 53 493]] Scoring Summary: (0) norm = 69.9778% (2) nneo = 60.3254% (3) infl = 85.7143% (5) dcis = 91.7695% (6) indc = 89.4397% (8) bckg = 58.2910% avg lbls = 79.4453% avg bckg = 58.2910% score = 77.3299% <** ========================================================================