================================================================= hyp_xgb_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[2071 0 5 0 0 0 0 0 31] [ 0 0 0 0 0 0 0 0 0] [ 0 0 3078 0 0 0 0 0 55] [ 0 0 1 417 0 0 0 0 1] [ 0 0 0 0 0 0 0 0 0] [ 0 0 11 0 0 494 0 0 4] [ 0 0 10 0 0 0 729 0 7] [ 0 0 0 0 0 0 0 0 0] [ 0 0 9 0 0 0 0 0 2417]] Scoring Summary: (0) norm = 1.7086% (2) nneo = 1.7555% (3) infl = 0.4773% (5) dcis = 2.9470% (6) indc = 2.2788% (8) bckg = 0.3710% avg lbls = 1.8334% avg bckg = 0.3710% score = 1.6872% <** ================================================================= hyp_xgb_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 658 0 547 0 0 0 5 0 131] [ 0 0 0 0 0 0 0 0 0] [ 310 0 1254 9 0 0 5 0 238] [ 10 0 194 113 0 0 2 0 1] [ 0 0 0 0 0 0 0 0 0] [ 16 0 273 1 0 0 1 0 5] [ 54 0 232 7 0 2 1 0 6] [ 0 0 0 0 0 0 0 0 0] [ 64 0 79 2 0 0 5 0 1185]] Scoring Summary: (0) norm = 50.9321% (2) nneo = 30.9471% (3) infl = 64.6875% (5) dcis = 100.0000% (6) indc = 99.6689% (8) bckg = 11.2360% avg lbls = 69.2471% avg bckg = 11.2360% score = 63.4460% <** ================================================================= hyp_xgb_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 543 0 700 1 0 0 5 0 100] [ 0 0 0 0 0 0 0 0 0] [ 364 0 1663 9 0 1 8 0 168] [ 8 0 104 117 0 0 2 0 0] [ 0 0 0 0 0 0 0 0 0] [ 24 0 195 5 0 0 1 0 18] [ 27 0 426 5 0 0 3 0 3] [ 0 0 0 0 0 0 0 0 0] [ 56 0 54 1 0 0 0 0 1071]] Scoring Summary: (0) norm = 59.7480% (2) nneo = 24.8531% (3) infl = 49.3506% (5) dcis = 100.0000% (6) indc = 99.3534% (8) bckg = 9.3909% avg lbls = 66.6610% avg bckg = 9.3909% score = 60.9340% <** ======================================================================== ================================================================= hyp_eb7_train.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1781 0 278 2 0 6 11 0 29] [ 0 0 0 0 0 0 0 0 0] [ 228 0 2603 40 0 162 52 0 48] [ 1 0 6 400 0 1 11 0 0] [ 0 0 0 0 0 0 0 0 0] [ 1 0 47 0 0 442 17 0 2] [ 1 0 24 17 0 17 682 0 5] [ 0 0 0 0 0 0 0 0 0] [ 16 0 44 5 0 1 3 0 2357]] Scoring Summary: (0) norm = 15.4722% (2) nneo = 16.9167% (3) infl = 4.5346% (5) dcis = 13.1631% (6) indc = 8.5791% (8) bckg = 2.8442% avg lbls = 11.7331% avg bckg = 2.8442% score = 10.8442% <** ================================================================= hyp_eb7_dev.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[1104 0 189 2 0 1 2 0 43] [ 0 0 0 0 0 0 0 0 0] [ 155 0 1519 17 0 83 11 0 31] [ 2 0 4 306 0 0 8 0 0] [ 0 0 0 0 0 0 0 0 0] [ 1 0 36 2 0 249 5 0 3] [ 1 0 8 8 0 6 278 0 1] [ 0 0 0 0 0 0 0 0 0] [ 32 0 43 4 0 1 2 0 1253]] Scoring Summary: (0) norm = 17.6734% (2) nneo = 16.3546% (3) infl = 4.3750% (5) dcis = 15.8784% (6) indc = 7.9470% (8) bckg = 6.1423% avg lbls = 12.4457% avg bckg = 6.1423% score = 11.8153% <** ================================================================= hyp_eb7_eval.csv : Legend: 0 = norm <** 1 = artf 2 = nneo <** 3 = infl <** 4 = susp 5 = dcis <** 6 = indc <** 7 = null 8 = bckg <** Confusion Matrix: [[ 914 0 360 1 0 4 9 0 61] [ 0 0 0 0 0 0 0 0 0] [ 346 0 1595 38 0 101 64 0 69] [ 1 0 7 213 0 1 9 0 0] [ 0 0 0 0 0 0 0 0 0] [ 10 0 100 2 0 99 14 0 18] [ 4 0 149 22 0 34 253 0 2] [ 0 0 0 0 0 0 0 0 0] [ 25 0 53 0 0 1 7 0 1096]] Scoring Summary: (0) norm = 32.2461% (2) nneo = 27.9259% (3) infl = 7.7922% (5) dcis = 59.2593% (6) indc = 45.4741% (8) bckg = 7.2758% avg lbls = 34.5395% avg bckg = 7.2758% score = 31.8131% <** ========================================================================