Models generated by SIMON

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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