================================================================= hyp_gbm_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1194 0 762 3 0 0 3 0 145] [ 0 0 0 0 0 0 0 0 0] [ 575 0 2251 29 0 7 29 0 242] [ 30 0 196 185 0 1 6 0 1] [ 0 0 0 0 0 0 0 0 0] [ 38 0 433 15 0 10 4 0 9] [ 143 0 512 22 0 4 47 0 18] [ 0 0 0 0 0 0 0 0 0] [ 66 0 92 1 0 0 6 0 2261]] Scoring Summary: (0) norm = 43.3318% (2) nneo = 28.1519% (3) infl = 55.8473% (5) dcis = 98.0354% (6) indc = 93.6997% (8) bckg = 6.8013% avg lbls = 63.8132% avg bckg = 6.8013% score = 58.1120% <** ================================================================= hyp_gbm_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 757 0 448 0 0 0 4 0 132] [ 0 0 0 0 0 0 0 0 0] [ 347 0 1228 4 0 5 10 0 222] [ 15 0 137 163 0 1 2 0 2] [ 0 0 0 0 0 0 0 0 0] [ 14 0 266 2 0 5 5 0 4] [ 50 0 225 8 0 2 12 0 5] [ 0 0 0 0 0 0 0 0 0] [ 51 0 80 1 0 0 2 0 1201]] Scoring Summary: (0) norm = 43.5496% (2) nneo = 32.3789% (3) infl = 49.0625% (5) dcis = 98.3108% (6) indc = 96.0265% (8) bckg = 10.0375% avg lbls = 63.8656% avg bckg = 10.0375% score = 58.4828% <** ================================================================= hyp_gbm_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 638 0 595 4 0 1 4 0 107] [ 0 0 0 0 0 0 0 0 0] [ 420 0 1609 17 0 7 11 0 149] [ 6 0 83 139 0 2 1 0 0] [ 0 0 0 0 0 0 0 0 0] [ 36 0 180 3 0 1 3 0 20] [ 30 0 398 9 0 9 15 0 3] [ 0 0 0 0 0 0 0 0 0] [ 32 0 62 1 0 0 1 0 1086]] Scoring Summary: (0) norm = 52.7057% (2) nneo = 27.2933% (3) infl = 39.8268% (5) dcis = 99.5885% (6) indc = 96.7672% (8) bckg = 8.1218% avg lbls = 63.2363% avg bckg = 8.1218% score = 57.7249% <** ======================================================================== ================================================================= hyp_cnn_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1584 0 380 8 0 10 56 0 69] [ 0 0 0 0 0 0 0 0 0] [ 426 0 1919 95 0 399 141 0 153] [ 0 0 2 415 0 0 2 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 0 1 0 0 508 0 0 0] [ 0 0 0 17 0 0 729 0 0] [ 0 0 0 0 0 0 0 0 0] [ 26 0 37 6 0 10 39 0 2308]] Scoring Summary: (0) norm = 24.8220% (2) nneo = 38.7488% (3) infl = 0.9547% (5) dcis = 0.1965% (6) indc = 2.2788% (8) bckg = 4.8640% avg lbls = 13.4002% avg bckg = 4.8640% score = 12.5465% <** ================================================================= 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: [[ 902 0 262 10 0 23 37 0 107] [ 0 0 0 0 0 0 0 0 0] [ 376 0 933 56 0 244 94 0 113] [ 5 0 26 274 0 0 14 0 1] [ 0 0 0 0 0 0 0 0 0] [ 2 0 81 7 0 185 16 0 5] [ 3 0 51 14 0 39 193 0 2] [ 0 0 0 0 0 0 0 0 0] [ 35 0 25 6 0 7 35 0 1227]] Scoring Summary: (0) norm = 32.7368% (2) nneo = 48.6233% (3) infl = 14.3750% (5) dcis = 37.5000% (6) indc = 36.0927% (8) bckg = 8.0899% avg lbls = 33.8656% avg bckg = 8.0899% score = 31.2880% <** ================================================================= 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: [[ 810 0 352 8 0 32 54 0 93] [ 0 0 0 0 0 0 0 0 0] [ 471 0 1165 73 0 255 154 0 95] [ 3 0 9 205 0 1 13 0 0] [ 0 0 0 0 0 0 0 0 0] [ 1 0 57 5 0 139 19 0 22] [ 5 0 119 33 0 134 172 0 1] [ 0 0 0 0 0 0 0 0 0] [ 19 0 25 0 0 9 26 0 1103]] Scoring Summary: (0) norm = 39.9555% (2) nneo = 47.3565% (3) infl = 11.2554% (5) dcis = 42.7984% (6) indc = 62.9310% (8) bckg = 6.6836% avg lbls = 40.8594% avg bckg = 6.6836% score = 37.4418% <** ========================================================================