================================================================= 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: [[2050 1 26 0 0 0 0 0 30] [ 0 0 0 0 0 0 0 0 0] [ 0 0 3104 0 0 0 0 0 29] [ 0 0 0 419 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0] [ 0 0 2 0 0 505 0 0 2] [ 0 0 16 0 0 0 728 0 2] [ 0 0 0 0 0 0 0 0 0] [ 0 0 1 0 0 0 0 0 2425]] Scoring Summary: (0) norm = 2.7053% (2) nneo = 0.9256% (3) infl = 0.0000% (5) dcis = 0.7859% (6) indc = 2.4129% (8) bckg = 0.0412% avg lbls = 1.3659% avg bckg = 0.0412% score = 1.2335% <** ================================================================= 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: [[ 720 0 493 0 0 0 2 0 126] [ 0 0 0 0 0 0 0 0 0] [ 325 2 1257 1 0 4 11 1 215] [ 16 0 186 92 0 3 18 3 2] [ 0 0 0 0 0 0 0 0 0] [ 15 0 268 0 0 2 6 0 5] [ 45 0 233 5 0 2 12 0 5] [ 0 0 0 0 0 0 0 0 0] [ 61 1 88 0 0 1 2 1 1181]] Scoring Summary: (0) norm = 46.3087% (2) nneo = 30.7819% (3) infl = 71.2500% (5) dcis = 99.3243% (6) indc = 96.0265% (8) bckg = 11.5356% avg lbls = 68.7383% avg bckg = 11.5356% score = 63.0180% <** ================================================================= 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: [[ 577 2 671 1 0 0 7 0 91] [ 0 0 0 0 0 0 0 0 0] [ 377 3 1647 5 0 5 17 2 157] [ 9 0 131 85 0 1 5 0 0] [ 0 0 0 0 0 0 0 0 0] [ 34 0 184 3 0 0 2 0 20] [ 24 0 395 2 0 5 28 7 3] [ 0 0 0 0 0 0 0 0 0] [ 39 9 62 0 0 0 4 2 1066]] Scoring Summary: (0) norm = 57.2276% (2) nneo = 25.5761% (3) infl = 63.2035% (5) dcis = 100.0000% (6) indc = 93.9655% (8) bckg = 9.8139% avg lbls = 67.9945% avg bckg = 9.8139% score = 62.1765% <** ======================================================================== ================================================================= 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: [[ 272 63 964 120 0 28 124 22 514] [ 0 0 0 0 0 0 0 0 0] [ 418 84 1508 173 0 31 154 41 724] [ 45 16 215 21 0 3 28 14 77] [ 0 0 0 0 0 0 0 0 0] [ 60 13 236 32 0 7 26 6 129] [ 97 22 360 39 0 7 39 12 170] [ 0 0 0 0 0 0 0 0 0] [ 279 78 1164 155 0 15 135 45 555]] Scoring Summary: (0) norm = 87.0907% (2) nneo = 51.8672% (3) infl = 94.9881% (5) dcis = 98.6248% (6) indc = 94.7721% (8) bckg = 77.1228% avg lbls = 85.4686% avg bckg = 77.1228% score = 84.6340% <** ================================================================= 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: [[ 550 1 672 5 0 3 30 1 79] [ 0 0 0 0 0 0 0 0 0] [ 180 3 1399 49 0 23 60 4 98] [ 1 0 46 271 0 0 2 0 0] [ 0 0 0 0 0 0 0 0 0] [ 6 0 229 10 0 24 17 2 8] [ 0 0 173 19 0 6 97 5 2] [ 0 0 0 0 0 0 0 0 0] [ 29 1 145 2 0 0 11 8 1139]] Scoring Summary: (0) norm = 58.9858% (2) nneo = 22.9626% (3) infl = 15.3125% (5) dcis = 91.8919% (6) indc = 67.8808% (8) bckg = 14.6816% avg lbls = 51.4067% avg bckg = 14.6816% score = 47.7342% <** ================================================================= 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: [[ 611 8 557 11 4 18 39 13 88] [ 0 0 0 0 0 0 0 0 0] [ 487 23 1275 60 9 62 123 51 123] [ 6 0 17 190 0 0 11 7 0] [ 0 0 0 0 0 0 0 0 0] [ 20 2 138 4 0 15 20 2 42] [ 40 2 209 38 1 20 112 38 4] [ 0 0 0 0 0 0 0 0 0] [ 24 42 103 0 0 2 8 10 993]] Scoring Summary: (0) norm = 54.7072% (2) nneo = 42.3859% (3) infl = 17.7489% (5) dcis = 93.8272% (6) indc = 75.8621% (8) bckg = 15.9898% avg lbls = 56.9062% avg bckg = 15.9898% score = 52.8146% <** ========================================================================