Here you can find information about all models generated by SIMON. Models can be downloaded and re-used for predictions. Each dataset is stored in separate folder which contains all the models built for that dataset. Name format is: {modelName}.RData This file contains following information: - All training specific model data: folds, tuning parameters, etc - All predictions made with test dataset - Confusion matrix and all performance measures calculated - Features and their Variable Importance Scores Here is an example of RData file structure: List of 5 $ model_training_fit :List of 23 ..$ method : chr "bagEarth" ..$ modelInfo :List of 15 .. ..$ label : chr "Bagged MARS" .. ..$ library : chr "earth" .. ..$ type : chr [1:2] "Regression" "Classification" .. ..$ parameters:'data.frame': 2 obs. of 3 variables: .. .. ..$ parameter: Factor w/ 2 levels "degree","nprune": 2 1 .. .. ..$ class : Factor w/ 1 level "numeric": 1 1 .. .. ..$ label : Factor w/ 2 levels "#Terms","Product Degree": 1 2 .. ..$ grid :function (x, y, len = NULL, search = "grid") .. ..$ loop :function (grid) .. ..$ fit :function (x, y, wts, param, lev, last, classProbs, ...) .. ..$ predict :function (modelFit, newdata, submodels = NULL) .. ..$ prob :function (modelFit, newdata, submodels = NULL) .. ..$ predictors:function (x, ...) .. ..$ varImp :function (object, ...) .. ..$ levels :function (x) .. ..$ tags : chr [1:5] "Multivariate Adaptive Regression Splines" "Ensemble Model" "Implicit Feature Selection" "Bagging" ... .. ..$ sort :function (x) .. ..$ oob :function (x) ..$ modelType : chr "Classification" ..$ results :'data.frame': 3 obs. of 24 variables: .. ..$ degree : num [1:3] 1 1 1 .. ..$ nprune : num [1:3] 2 10 18 .. ..$ logLoss : num [1:3] 1.27 1.84 1.66 .. ..$ AUC : num [1:3] 0.694 0.75 0.695 .. ..$ Accuracy : num [1:3] 0.623 0.698 0.657 .. ..$ Kappa : num [1:3] 0.12 0.36 0.262 .. ..$ F1 : num [1:3] 0.46 0.614 0.542 .. ..$ Sensitivity : num [1:3] 0.217 0.589 0.517 .. ..$ Specificity : num [1:3] 0.895 0.765 0.743 .. ..$ Pos_Pred_Value : num [1:3] 0.606 0.655 0.6 .. ..$ Neg_Pred_Value : num [1:3] 0.636 0.76 0.715 .. ..$ Detection_Rate : num [1:3] 0.0864 0.238 0.2098 .. ..$ Balanced_Accuracy : num [1:3] 0.556 0.677 0.63 .. ..$ logLossSD : num [1:3] 0.188 0.693 0.562 .. ..$ AUCSD : num [1:3] 0.19 0.146 0.157 .. ..$ AccuracySD : num [1:3] 0.0922 0.1339 0.1302 .. ..$ KappaSD : num [1:3] 0.217 0.28 0.279 .. ..$ F1SD : num [1:3] 0.099 0.174 0.176 .. ..$ SensitivitySD : num [1:3] 0.204 0.246 0.266 .. ..$ SpecificitySD : num [1:3] 0.12 0.194 0.182 .. ..$ Pos_Pred_ValueSD : num [1:3] 0.369 0.257 0.235 .. ..$ Neg_Pred_ValueSD : num [1:3] 0.0711 0.1264 0.137 .. ..$ Detection_RateSD : num [1:3] 0.0818 0.1114 0.1167 .. ..$ Balanced_AccuracySD: num [1:3] 0.0996 0.1358 0.1406 ..$ pred :'data.frame': 720 obs. of 8 variables: .. ..$ pred : Factor w/ 2 levels "high","low": 2 1 2 2 2 2 1 2 2 2 ... .. ..$ obs : Factor w/ 2 levels "high","low": 1 1 2 2 2 2 1 1 1 2 ... .. ..$ rowIndex: int [1:720] 4 26 34 39 43 47 65 4 26 34 ... .. ..$ high : num [1:720] 0.415 0.822 0.39 0.276 0.135 ... .. ..$ low : num [1:720] 0.585 0.178 0.61 0.724 0.865 ... .. ..$ degree : num [1:720] 1 1 1 1 1 1 1 1 1 1 ... .. ..$ nprune : num [1:720] 18 18 18 18 18 18 18 2 2 2 ... .. ..$ Resample: chr [1:720] "Fold01.Rep1" "Fold01.Rep1" "Fold01.Rep1" "Fold01.Rep1" ... ..$ bestTune :'data.frame': 1 obs. of 2 variables: .. ..$ nprune: num 10 .. ..$ degree: num 1 ..$ call : language train.formula(form = factor(outcome) ~ ., data = training, method = model, trControl = trControl, preProcess = NU| __truncated__ ..$ dots : list() ..$ metric : chr "Accuracy" ..$ control :List of 27 .. ..$ method : chr "repeatedcv" .. ..$ number : num 10 .. ..$ repeats : num 3 .. ..$ search : chr "grid" .. ..$ p : num 0.75 .. ..$ initialWindow : NULL .. ..$ horizon : num 1 .. ..$ fixedWindow : logi TRUE .. ..$ skip : num 0 .. ..$ verboseIter : logi FALSE .. ..$ returnData : logi TRUE .. ..$ returnResamp : chr "final" .. ..$ savePredictions : chr "all" .. ..$ classProbs : logi TRUE .. ..$ summaryFunction :function (data, lev = NULL, model = NULL) .. ..$ selectionFunction: chr "best" .. ..$ preProcOptions :List of 6 .. .. ..$ thresh : num 0.95 .. .. ..$ ICAcomp : num 3 .. .. ..$ k : num 5 .. .. ..$ freqCut : num 19 .. .. ..$ uniqueCut: num 10 .. .. ..$ cutoff : num 0.9 .. ..$ sampling : NULL .. ..$ index :List of 30 .. .. ..$ Fold01.Rep1: int [1:73] 1 2 3 5 6 7 8 9 10 11 ... .. .. ..$ Fold02.Rep1: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold03.Rep1: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold04.Rep1: int [1:71] 1 2 3 4 5 7 8 9 10 11 ... .. .. ..$ Fold05.Rep1: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold06.Rep1: int [1:72] 1 2 4 6 7 8 9 10 11 12 ... .. .. ..$ Fold07.Rep1: int [1:73] 1 3 4 5 6 7 8 9 10 11 ... .. .. ..$ Fold08.Rep1: int [1:71] 1 2 3 4 5 6 7 9 10 11 ... .. .. ..$ Fold09.Rep1: int [1:72] 1 2 3 4 5 6 7 8 10 11 ... .. .. ..$ Fold10.Rep1: int [1:72] 2 3 4 5 6 8 9 12 13 14 ... .. .. ..$ Fold01.Rep2: int [1:72] 1 2 4 5 6 7 8 9 10 11 ... .. .. ..$ Fold02.Rep2: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold03.Rep2: int [1:72] 1 2 3 4 5 6 7 9 10 11 ... .. .. ..$ Fold04.Rep2: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold05.Rep2: int [1:71] 1 2 3 4 5 6 7 8 9 11 ... .. .. ..$ Fold06.Rep2: int [1:71] 1 2 3 5 6 7 8 9 10 11 ... .. .. ..$ Fold07.Rep2: int [1:73] 1 3 4 5 6 8 9 10 11 12 ... .. .. ..$ Fold08.Rep2: int [1:73] 2 3 4 5 6 7 8 9 10 11 ... .. .. ..$ Fold09.Rep2: int [1:72] 1 2 3 4 5 6 7 8 10 12 ... .. .. ..$ Fold10.Rep2: int [1:72] 1 2 3 4 7 8 9 10 11 12 ... .. .. ..$ Fold01.Rep3: int [1:72] 1 3 4 6 7 8 9 10 11 12 ... .. .. ..$ Fold02.Rep3: int [1:73] 1 2 3 4 5 6 7 8 10 11 ... .. .. ..$ Fold03.Rep3: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold04.Rep3: int [1:72] 1 2 3 5 6 7 8 9 10 11 ... .. .. ..$ Fold05.Rep3: int [1:72] 2 3 4 5 6 7 8 9 10 11 ... .. .. ..$ Fold06.Rep3: int [1:72] 1 2 3 4 5 6 7 9 10 12 ... .. .. ..$ Fold07.Rep3: int [1:72] 1 2 3 4 5 6 8 9 10 11 ... .. .. ..$ Fold08.Rep3: int [1:71] 1 2 4 5 7 8 9 10 11 13 ... .. .. ..$ Fold09.Rep3: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold10.Rep3: int [1:72] 1 2 3 4 5 6 7 8 9 11 ... .. ..$ indexOut :List of 30 .. .. ..$ Resample01: int [1:7] 4 26 34 39 43 47 65 .. .. ..$ Resample02: int [1:8] 24 28 45 56 64 69 72 78 .. .. ..$ Resample03: int [1:8] 20 23 27 40 50 53 57 66 .. .. ..$ Resample04: int [1:9] 6 21 38 46 49 51 54 67 77 .. .. ..$ Resample05: int [1:8] 14 17 42 48 52 62 76 79 .. .. ..$ Resample06: int [1:8] 3 5 15 18 19 36 37 73 .. .. ..$ Resample07: int [1:7] 2 29 33 58 59 71 80 .. .. ..$ Resample08: int [1:9] 8 13 22 30 31 32 35 61 68 .. .. ..$ Resample09: int [1:8] 9 12 44 55 60 70 74 75 .. .. ..$ Resample10: int [1:8] 1 7 10 11 16 25 41 63 .. .. ..$ Resample11: int [1:8] 3 24 27 28 39 53 55 77 .. .. ..$ Resample12: int [1:8] 14 16 36 41 46 59 69 73 .. .. ..$ Resample13: int [1:8] 8 17 31 50 63 70 71 80 .. .. ..$ Resample14: int [1:8] 19 25 35 52 54 58 65 72 .. .. ..$ Resample15: int [1:9] 10 12 13 23 32 38 48 76 78 .. .. ..$ Resample16: int [1:9] 4 21 22 33 34 44 64 67 75 .. .. ..$ Resample17: int [1:7] 2 7 42 49 51 60 79 .. .. ..$ Resample18: int [1:7] 1 15 26 29 37 40 57 .. .. ..$ Resample19: int [1:8] 9 11 18 45 47 56 62 66 .. .. ..$ Resample20: int [1:8] 5 6 20 30 43 61 68 74 .. .. ..$ Resample21: int [1:8] 2 5 34 38 49 53 54 74 .. .. ..$ Resample22: int [1:7] 9 19 26 27 32 70 78 .. .. ..$ Resample23: int [1:8] 17 33 36 46 48 52 64 73 .. .. ..$ Resample24: int [1:8] 4 13 18 21 35 58 63 71 .. .. ..$ Resample25: int [1:8] 1 20 24 28 30 50 55 65 .. .. ..$ Resample26: int [1:8] 8 11 15 22 62 66 72 75 .. .. ..$ Resample27: int [1:8] 7 14 25 31 40 47 59 79 .. .. ..$ Resample28: int [1:9] 3 6 12 42 43 60 69 77 80 .. .. ..$ Resample29: int [1:8] 23 29 41 45 56 57 67 68 .. .. ..$ Resample30: int [1:8] 10 16 37 39 44 51 61 76 .. ..$ indexFinal : NULL .. ..$ timingSamps : num 0 .. ..$ predictionBounds : logi [1:2] FALSE FALSE .. ..$ seeds :List of 31 .. .. ..$ : int [1:9] 114 622 609 999 858 638 10 231 661 .. .. ..$ : int [1:9] 515 693 544 282 920 291 833 285 265 .. .. ..$ : int [1:9] 187 232 316 302 159 40 218 805 522 .. .. ..$ : int [1:9] 915 831 46 455 265 304 505 180 754 .. .. ..$ : int [1:9] 202 259 991 805 552 644 310 618 328 .. .. ..$ : int [1:9] 502 677 485 244 763 74 308 713 501 .. .. ..$ : int [1:9] 153 504 493 749 174 845 860 42 315 .. .. ..$ : int [1:9] 14 239 706 308 507 52 562 121 886 .. .. ..$ : int [1:9] 15 783 90 518 383 70 319 664 919 .. .. ..$ : int [1:9] 472 143 544 196 895 388 310 159 890 .. .. ..$ : int [1:9] 167 900 134 132 105 510 299 27 308 .. .. ..$ : int [1:9] 743 36 564 280 204 134 324 154 129 .. .. ..$ : int [1:9] 436 39 712 101 947 122 219 907 939 .. .. ..$ : int [1:9] 280 124 796 743 913 990 937 483 282 .. .. ..$ : int [1:9] 252 503 496 318 959 631 127 421 908 .. .. ..$ : int [1:9] 468 908 597 630 866 501 978 323 478 .. .. ..$ : int [1:9] 357 627 741 565 977 574 437 227 82 .. .. ..$ : int [1:9] 851 235 987 601 995 374 552 427 572 .. .. ..$ : int [1:9] 433 225 85 636 430 73 798 324 752 .. .. ..$ : int [1:9] 585 709 427 343 757 422 558 116 301 .. .. ..$ : int [1:9] 479 345 600 76 953 23 837 629 308 .. .. ..$ : int [1:9] 743 639 991 128 880 807 817 829 727 .. .. ..$ : int [1:9] 984 639 660 527 317 765 524 728 306 .. .. ..$ : int [1:9] 405 205 984 565 280 185 754 563 925 .. .. ..$ : int [1:9] 639 701 479 848 421 32 257 333 133 .. .. ..$ : int [1:9] 500 802 337 508 493 794 564 106 999 .. .. ..$ : int [1:9] 568 213 749 307 488 985 422 243 216 .. .. ..$ : int [1:9] 690 980 477 772 573 962 793 529 592 .. .. ..$ : int [1:9] 264 280 65 562 262 4 586 517 838 .. .. ..$ : int [1:9] 30 600 268 121 101 745 16 50 742 .. .. ..$ : int 358 .. ..$ adaptive :List of 4 .. .. ..$ min : num 5 .. .. ..$ alpha : num 0.05 .. .. ..$ method : chr "gls" .. .. ..$ complete: logi TRUE .. ..$ trim : logi FALSE .. ..$ allowParallel : logi TRUE ..$ trainingData:'data.frame': 80 obs. of 13 variables: .. ..$ .outcome : Factor w/ 2 levels "high","low": 2 2 1 1 1 1 1 1 1 1 ... .. ..$ CD161_pos_CD45RA_pos_Tregs : num [1:80] 1.68 0.84 0.43 0.56 0.73 0.64 0.53 1.15 0.51 1.38 ... .. ..$ CD27_pos_CD8_pos_T_cells : num [1:80] 85.2 71.9 84.5 83 74.8 66.4 87.7 64.1 87.3 89.5 ... .. ..$ CD85j_pos_CD8_pos_T_cells : num [1:80] 17.7 25.8 17.1 19.1 19.1 28.6 8.31 18.8 11 6.95 ... .. ..$ CD94_pos_CD8_pos_T_cells : num [1:80] 4.31 14.2 3.94 4.48 10.1 25.8 20.3 11 4.16 2.74 ... .. ..$ central_memory_CD8_pos_T_cells: num [1:80] 1.96 3.27 2.77 6.31 7.59 6.02 8.54 5.64 6.36 2.93 ... .. ..$ effector_CD8_pos_T_cells : num [1:80] 14.7 26.9 13.4 11.7 21 18.8 10.6 14.4 6.82 7.72 ... .. ..$ L50_EOTAXIN : num [1:80] -0.14 1.3 0.28 -0.76 0.16 0.4 0.17 0.88 0.73 0.84 ... .. ..$ L50_HGF : num [1:80] -0.06 1.45 -0.14 -1.12 -0.36 0.19 0.1 0.82 1.14 1.35 ... .. ..$ L50_IL7 : num [1:80] -0.11 1.49 -0.1 -0.88 0.07 0.23 0.18 0.99 0.97 1.26 ... .. ..$ L50_MCP3 : num [1:80] -1.38 2 -0.17 0.48 -0.54 1.03 0.8 0.43 1.06 0.77 ... .. ..$ L50_TRAIL : num [1:80] 0.17 1.8 0.21 -1.56 0.34 0.96 0 -0.59 1.43 1.65 ... .. ..$ monocytes : num [1:80] 17.1 12 20.5 21.2 13.4 15.9 18.2 12.7 14 14.6 ... ..$ resample :'data.frame': 30 obs. of 12 variables: .. ..$ logLoss : num [1:30] 2.89 2.01 1.22 3.06 1.56 ... .. ..$ AUC : num [1:30] 0.933 0.867 0.833 1 0.8 ... .. ..$ Accuracy : num [1:30] 0.875 0.625 0.857 0.889 0.625 ... .. ..$ Kappa : num [1:30] 0.714 0.143 0.696 0.769 0.143 ... .. ..$ F1 : num [1:30] 0.8 0.4 0.8 0.857 0.4 ... .. ..$ Sensitivity : num [1:30] 0.667 0.333 0.667 0.75 0.333 ... .. ..$ Specificity : num [1:30] 1 0.8 1 1 0.8 0.6 0.6 1 0.6 1 ... .. ..$ Pos_Pred_Value : num [1:30] 1 0.5 1 1 0.5 ... .. ..$ Neg_Pred_Value : num [1:30] 0.833 0.667 0.8 0.833 0.667 ... .. ..$ Detection_Rate : num [1:30] 0.25 0.125 0.286 0.333 0.125 ... .. ..$ Balanced_Accuracy: num [1:30] 0.833 0.567 0.833 0.875 0.567 ... .. ..$ Resample : chr [1:30] "Fold03.Rep1" "Fold02.Rep1" "Fold01.Rep1" "Fold04.Rep1" ... ..$ resampledCM :'data.frame': 90 obs. of 7 variables: .. ..$ degree : num [1:90] 1 1 1 1 1 1 1 1 1 1 ... .. ..$ nprune : num [1:90] 18 2 10 18 2 10 18 2 10 18 ... .. ..$ cell1 : num [1:90] 2 0 2 1 1 1 1 1 2 2 ... .. ..$ cell2 : num [1:90] 1 3 1 2 2 2 2 2 1 2 ... .. ..$ cell3 : num [1:90] 0 0 0 1 1 1 1 0 0 0 ... .. ..$ cell4 : num [1:90] 4 4 4 4 4 4 4 5 5 5 ... .. ..$ Resample: chr [1:90] "Fold01.Rep1" "Fold01.Rep1" "Fold01.Rep1" "Fold02.Rep1" ... ..$ perfNames : chr [1:11] "logLoss" "AUC" "Accuracy" "Kappa" ... ..$ maximize : logi TRUE ..$ yLimits : NULL ..$ times :List of 3 .. ..$ everything: 'proc_time' Named num [1:5] 2.25 0.56 13.92 156.52 8.29 .. .. ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ... .. ..$ final : 'proc_time' Named num [1:5] 0.776 0.004 0.783 0 0 .. .. ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ... .. ..$ prediction: logi [1:3] NA NA NA ..$ levels : chr [1:2] "high" "low" .. ..- attr(*, "ordered")= logi FALSE ..$ terms :Classes 'terms', 'formula' language factor(outcome) ~ CD161_pos_CD45RA_pos_Tregs + CD27_pos_CD8_pos_T_cells + CD85j_pos_CD8_pos_T_cells + CD94_pos_CD| __truncated__ $ model_prediction :List of 2 ..$ pred_prob:'data.frame': 25 obs. of 2 variables: .. ..$ high: num [1:25] 0.237 0.52 0.625 0.857 0.452 ... .. ..$ low : num [1:25] 0.763 0.48 0.375 0.143 0.548 ... ..$ pred_raw : Factor w/ 2 levels "high","low": 2 1 1 1 2 2 1 1 1 2 ... $ roc_auc :List of 2 ..$ roc_p:List of 15 .. ..$ percent : logi FALSE .. ..$ sensitivities : num [1:26] 1 1 1 0.933 0.867 ... .. ..$ specificities : num [1:26] 0 0.1 0.2 0.2 0.2 0.3 0.4 0.5 0.5 0.5 ... .. ..$ thresholds : num [1:26] -Inf 0.0625 0.0688 0.0753 0.122 ... .. ..$ direction : chr "<" .. ..$ cases : num [1:15] 0.52 0.625 0.452 0.55 0.735 ... .. ..$ controls : num [1:10] 0.237 0.857 0.238 0.354 0.167 ... .. ..$ fun.sesp :function (thresholds, controls, cases, direction) .. ..$ auc : 'auc' num 0.7 .. .. ..- attr(*, "partial.auc")= logi FALSE .. .. ..- attr(*, "percent")= logi FALSE .. .. ..- attr(*, "roc")=List of 15 .. .. .. ..$ percent : logi FALSE .. .. .. ..$ sensitivities : num [1:26] 1 1 1 0.933 0.867 ... .. .. .. ..$ specificities : num [1:26] 0 0.1 0.2 0.2 0.2 0.3 0.4 0.5 0.5 0.5 ... .. .. .. ..$ thresholds : num [1:26] -Inf 0.0625 0.0688 0.0753 0.122 ... .. .. .. ..$ direction : chr "<" .. .. .. ..$ cases : num [1:15] 0.52 0.625 0.452 0.55 0.735 ... .. .. .. ..$ controls : num [1:10] 0.237 0.857 0.238 0.354 0.167 ... .. .. .. ..$ fun.sesp :function (thresholds, controls, cases, direction) .. .. .. ..$ auc : 'auc' num 0.7 .. .. .. .. ..- attr(*, "partial.auc")= logi FALSE .. .. .. .. ..- attr(*, "percent")= logi FALSE .. .. .. .. ..- attr(*, "roc")=List of 8 .. .. .. .. .. ..$ percent : logi FALSE .. .. .. .. .. ..$ sensitivities: num [1:26] 1 1 1 0.933 0.867 ... .. .. .. .. .. ..$ specificities: num [1:26] 0 0.1 0.2 0.2 0.2 0.3 0.4 0.5 0.5 0.5 ... .. .. .. .. .. ..$ thresholds : num [1:26] -Inf 0.0625 0.0688 0.0753 0.122 ... .. .. .. .. .. ..$ direction : chr "<" .. .. .. .. .. ..$ cases : num [1:15] 0.52 0.625 0.452 0.55 0.735 ... .. .. .. .. .. ..$ controls : num [1:10] 0.237 0.857 0.238 0.354 0.167 ... .. .. .. .. .. ..$ fun.sesp :function (thresholds, controls, cases, direction) .. .. .. .. .. ..- attr(*, "class")= chr "roc" .. .. .. ..$ call : language roc.default(response = testing$outcome, predictor = predict_model[, "high"], levels = levels(testing$outcome)) .. .. .. ..$ original.predictor: num [1:25] 0.237 0.52 0.625 0.857 0.452 ... .. .. .. ..$ original.response : Factor w/ 2 levels "high","low": 1 2 2 1 2 1 2 2 2 2 ... .. .. .. ..$ predictor : num [1:25] 0.237 0.52 0.625 0.857 0.452 ... .. .. .. ..$ response : Factor w/ 2 levels "high","low": 1 2 2 1 2 1 2 2 2 2 ... .. .. .. ..$ levels : chr [1:2] "high" "low" .. .. .. ..- attr(*, "class")= chr "roc" .. ..$ call : language roc.default(response = testing$outcome, predictor = predict_model[, "high"], levels = levels(testing$outcome)) .. ..$ original.predictor: num [1:25] 0.237 0.52 0.625 0.857 0.452 ... .. ..$ original.response : Factor w/ 2 levels "high","low": 1 2 2 1 2 1 2 2 2 2 ... .. ..$ predictor : num [1:25] 0.237 0.52 0.625 0.857 0.452 ... .. ..$ response : Factor w/ 2 levels "high","low": 1 2 2 1 2 1 2 2 2 2 ... .. ..$ levels : chr [1:2] "high" "low" .. ..- attr(*, "class")= chr "roc" ..$ auc_p: num 0.7 $ confusion_matrix :List of 6 ..$ positive: chr "high" ..$ table : 'table' int [1:2, 1:2] 7 8 8 2 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:2] "low" "high" .. .. ..$ reference: chr [1:2] "low" "high" ..$ overall : Named num [1:7] 0.36 -0.333 0.18 0.575 0.6 ... .. ..- attr(*, "names")= chr [1:7] "Accuracy" "Kappa" "AccuracyLower" "AccuracyUpper" ... ..$ byClass : Named num [1:11] 0.2 0.467 0.2 0.467 0.2 ... .. ..- attr(*, "names")= chr [1:11] "Sensitivity" "Specificity" "Pos Pred Value" "Neg Pred Value" ... ..$ mode : chr "sens_spec" ..$ dots : list() ..- attr(*, "class")= chr "confusionMatrix" $ variable_importance:'data.frame': 12 obs. of 4 variables: ..$ score_perc: num [1:12] 100 78.8 63.1 47.6 34.5 ... ..$ features : chr [1:12] "L50_EOTAXIN" "central_memory_CD8_pos_T_cells" "CD94_pos_CD8_pos_T_cells" "L50_TRAIL" ... ..$ rank : int [1:12] 1 2 3 4 5 6 7 8 9 10 ... ..$ score_no : num [1:12] 99.3 78.2 62.7 47.3 34.3 ... 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