This chapter presents the results obtained with the classifier trained in Chapter 5 .
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Loading required package: lattice
Attaching package: 'caret'
The following object is masked from 'package:purrr':
lift
First, we load the estimated models from Chapter 5 .
load ("../data/out/estim/v3/grid_search_rf.rda" )load ("../data/out/estim/v3/grid_search_xgb.rda" )load ("../data/out/estim/v3/svm_model.rda" )load ("../data/out/estim/v3/glmnet_model.rda" )
Grid Search Results
Summary
The summary of the grid search process can be displayed. The chosen hyperparameters are given at the end of each summary.
Random Forest
4235 samples
91 predictor
2 classes: 'Not_D_and_inf_Q1', 'Not_D_and_sup_Q1'
No pre-processing
Resampling: Cross-Validated (5 fold, repeated 10 times)
Summary of sample sizes: 3388, 3388, 3388, 3389, 3387, 3389, ...
Resampling results across tuning parameters:
mtry min.node.size ROC Sens Spec
3 50 0.6854680 0.06024847 0.9882306
3 75 0.6842574 0.04671599 0.9910716
3 100 0.6844836 0.03669975 0.9937432
3 150 0.6829967 0.02042494 0.9965842
4 50 0.6858963 0.08199694 0.9826174
4 75 0.6845153 0.06557138 0.9866419
4 100 0.6842896 0.05633732 0.9894142
4 150 0.6828222 0.03521201 0.9937773
5 50 0.6853200 0.09616268 0.9786600
5 75 0.6846941 0.08184375 0.9827192
5 100 0.6840973 0.06987500 0.9864383
5 150 0.6827223 0.04898407 0.9914097
6 50 0.6847254 0.10750460 0.9761240
6 75 0.6835288 0.09483333 0.9794042
6 100 0.6842504 0.08090809 0.9827861
6 150 0.6827904 0.05931066 0.9883664
7 50 0.6840431 0.11783395 0.9724375
7 75 0.6838224 0.10257721 0.9767664
7 100 0.6828217 0.08935509 0.9805205
7 150 0.6820516 0.07026471 0.9865064
8 50 0.6838515 0.12472181 0.9703744
8 75 0.6832326 0.11086979 0.9744661
8 100 0.6832729 0.09655147 0.9785250
8 150 0.6825983 0.07574020 0.9838348
9 50 0.6832056 0.13136857 0.9677365
9 75 0.6832825 0.11533119 0.9723028
9 100 0.6832821 0.10476716 0.9765643
9 150 0.6818508 0.08333058 0.9822452
Tuning parameter 'splitrule' was held constant at a value of gini
Sens was used to select the optimal model using the largest value.
The final values used for the model were mtry = 9, splitrule = gini
and min.node.size = 50.
eXtreme Gradient Boosting
4235 samples
91 predictor
2 classes: 'Not_D_and_inf_Q1', 'Not_D_and_sup_Q1'
No pre-processing
Resampling: Cross-Validated (5 fold, repeated 10 times)
Summary of sample sizes: 3388, 3389, 3387, 3389, 3387, 3388, ...
Resampling results across tuning parameters:
max_depth gamma colsample_bytree min_child_weight subsample ROC
3 0 0.1 50 0.7 0.6835323
3 0 0.1 50 0.8 0.6846741
3 0 0.1 50 0.9 0.6849190
3 0 0.1 50 1.0 0.6858939
3 0 0.1 100 0.7 0.6771646
3 0 0.1 100 0.8 0.6777002
3 0 0.1 100 0.9 0.6789286
3 0 0.1 100 1.0 0.6802923
3 0 0.1 150 0.7 0.6595120
3 0 0.1 150 0.8 0.6703930
3 0 0.1 150 0.9 0.6746262
3 0 0.1 150 1.0 0.6766152
3 0 0.2 50 0.7 0.6864756
3 0 0.2 50 0.8 0.6870874
3 0 0.2 50 0.9 0.6868075
3 0 0.2 50 1.0 0.6865789
3 0 0.2 100 0.7 0.6802905
3 0 0.2 100 0.8 0.6808479
3 0 0.2 100 0.9 0.6814301
3 0 0.2 100 1.0 0.6814727
3 0 0.2 150 0.7 0.6633344
3 0 0.2 150 0.8 0.6745859
3 0 0.2 150 0.9 0.6778468
3 0 0.2 150 1.0 0.6788249
3 0 0.3 50 0.7 0.6871162
3 0 0.3 50 0.8 0.6870904
3 0 0.3 50 0.9 0.6869967
3 0 0.3 50 1.0 0.6854663
3 0 0.3 100 0.7 0.6812380
3 0 0.3 100 0.8 0.6818300
3 0 0.3 100 0.9 0.6813210
3 0 0.3 100 1.0 0.6814551
3 0 0.3 150 0.7 0.6641503
3 0 0.3 150 0.8 0.6757664
3 0 0.3 150 0.9 0.6784423
3 0 0.3 150 1.0 0.6790995
3 0 0.4 50 0.7 0.6871565
3 0 0.4 50 0.8 0.6868829
3 0 0.4 50 0.9 0.6862778
3 0 0.4 50 1.0 0.6848754
3 0 0.4 100 0.7 0.6813881
3 0 0.4 100 0.8 0.6821184
3 0 0.4 100 0.9 0.6813171
3 0 0.4 100 1.0 0.6808612
3 0 0.4 150 0.7 0.6648116
3 0 0.4 150 0.8 0.6754444
3 0 0.4 150 0.9 0.6785908
3 0 0.4 150 1.0 0.6786125
3 0 0.5 50 0.7 0.6869111
3 0 0.5 50 0.8 0.6867778
3 0 0.5 50 0.9 0.6854914
3 0 0.5 50 1.0 0.6840334
3 0 0.5 100 0.7 0.6814563
3 0 0.5 100 0.8 0.6818431
3 0 0.5 100 0.9 0.6810024
3 0 0.5 100 1.0 0.6802620
3 0 0.5 150 0.7 0.6645502
3 0 0.5 150 0.8 0.6759271
3 0 0.5 150 0.9 0.6785300
3 0 0.5 150 1.0 0.6782090
3 0 0.6 50 0.7 0.6870920
3 0 0.6 50 0.8 0.6866718
3 0 0.6 50 0.9 0.6860527
3 0 0.6 50 1.0 0.6835393
3 0 0.6 100 0.7 0.6816430
3 0 0.6 100 0.8 0.6817530
3 0 0.6 100 0.9 0.6808054
3 0 0.6 100 1.0 0.6795081
3 0 0.6 150 0.7 0.6641105
3 0 0.6 150 0.8 0.6757353
3 0 0.6 150 0.9 0.6782596
3 0 0.6 150 1.0 0.6778046
3 0 0.7 50 0.7 0.6867026
3 0 0.7 50 0.8 0.6864445
3 0 0.7 50 0.9 0.6855883
3 0 0.7 50 1.0 0.6832436
3 0 0.7 100 0.7 0.6814912
3 0 0.7 100 0.8 0.6817369
3 0 0.7 100 0.9 0.6805055
3 0 0.7 100 1.0 0.6790237
3 0 0.7 150 0.7 0.6645184
3 0 0.7 150 0.8 0.6757382
3 0 0.7 150 0.9 0.6783979
3 0 0.7 150 1.0 0.6776318
3 0 0.8 50 0.7 0.6863782
3 0 0.8 50 0.8 0.6860718
3 0 0.8 50 0.9 0.6855278
3 0 0.8 50 1.0 0.6830230
3 0 0.8 100 0.7 0.6809104
3 0 0.8 100 0.8 0.6813190
3 0 0.8 100 0.9 0.6804634
3 0 0.8 100 1.0 0.6787946
3 0 0.8 150 0.7 0.6646043
3 0 0.8 150 0.8 0.6754229
3 0 0.8 150 0.9 0.6780978
3 0 0.8 150 1.0 0.6769792
3 0 0.9 50 0.7 0.6864509
3 0 0.9 50 0.8 0.6860605
3 0 0.9 50 0.9 0.6848746
3 0 0.9 50 1.0 0.6827182
3 0 0.9 100 0.7 0.6811243
3 0 0.9 100 0.8 0.6810076
3 0 0.9 100 0.9 0.6800833
3 0 0.9 100 1.0 0.6783560
3 0 0.9 150 0.7 0.6640517
3 0 0.9 150 0.8 0.6754073
3 0 0.9 150 0.9 0.6778474
3 0 0.9 150 1.0 0.6767668
3 0 1.0 50 0.7 0.6862640
3 0 1.0 50 0.8 0.6860456
3 0 1.0 50 0.9 0.6848614
3 0 1.0 50 1.0 0.6824156
3 0 1.0 100 0.7 0.6809020
3 0 1.0 100 0.8 0.6811924
3 0 1.0 100 0.9 0.6799430
3 0 1.0 100 1.0 0.6780553
3 0 1.0 150 0.7 0.6639681
3 0 1.0 150 0.8 0.6752783
3 0 1.0 150 0.9 0.6779278
3 0 1.0 150 1.0 0.6763591
3 5 0.1 50 0.7 0.6828140
3 5 0.1 50 0.8 0.6842965
3 5 0.1 50 0.9 0.6849028
3 5 0.1 50 1.0 0.6853866
3 5 0.1 100 0.7 0.6763572
3 5 0.1 100 0.8 0.6771185
3 5 0.1 100 0.9 0.6784831
3 5 0.1 100 1.0 0.6797941
3 5 0.1 150 0.7 0.6587939
3 5 0.1 150 0.8 0.6697473
3 5 0.1 150 0.9 0.6741116
3 5 0.1 150 1.0 0.6764173
3 5 0.2 50 0.7 0.6863963
3 5 0.2 50 0.8 0.6872963
3 5 0.2 50 0.9 0.6866868
3 5 0.2 50 1.0 0.6866366
3 5 0.2 100 0.7 0.6800511
3 5 0.2 100 0.8 0.6809275
3 5 0.2 100 0.9 0.6812956
3 5 0.2 100 1.0 0.6815251
3 5 0.2 150 0.7 0.6629999
3 5 0.2 150 0.8 0.6742652
3 5 0.2 150 0.9 0.6774612
3 5 0.2 150 1.0 0.6784466
3 5 0.3 50 0.7 0.6869515
3 5 0.3 50 0.8 0.6872420
3 5 0.3 50 0.9 0.6865540
3 5 0.3 50 1.0 0.6858744
3 5 0.3 100 0.7 0.6809379
3 5 0.3 100 0.8 0.6813783
3 5 0.3 100 0.9 0.6818276
3 5 0.3 100 1.0 0.6812894
3 5 0.3 150 0.7 0.6641493
3 5 0.3 150 0.8 0.6752267
3 5 0.3 150 0.9 0.6783444
3 5 0.3 150 1.0 0.6787567
3 5 0.4 50 0.7 0.6872199
3 5 0.4 50 0.8 0.6870581
3 5 0.4 50 0.9 0.6863795
3 5 0.4 50 1.0 0.6852869
3 5 0.4 100 0.7 0.6813060
3 5 0.4 100 0.8 0.6816249
3 5 0.4 100 0.9 0.6814106
3 5 0.4 100 1.0 0.6809197
3 5 0.4 150 0.7 0.6642683
3 5 0.4 150 0.8 0.6755784
3 5 0.4 150 0.9 0.6786048
3 5 0.4 150 1.0 0.6783614
3 5 0.5 50 0.7 0.6872155
3 5 0.5 50 0.8 0.6867043
3 5 0.5 50 0.9 0.6859378
3 5 0.5 50 1.0 0.6842065
3 5 0.5 100 0.7 0.6819643
3 5 0.5 100 0.8 0.6816489
3 5 0.5 100 0.9 0.6810673
3 5 0.5 100 1.0 0.6800658
3 5 0.5 150 0.7 0.6644405
3 5 0.5 150 0.8 0.6758188
3 5 0.5 150 0.9 0.6784204
3 5 0.5 150 1.0 0.6780425
3 5 0.6 50 0.7 0.6867639
3 5 0.6 50 0.8 0.6868521
3 5 0.6 50 0.9 0.6854597
3 5 0.6 50 1.0 0.6839325
3 5 0.6 100 0.7 0.6815962
3 5 0.6 100 0.8 0.6811540
3 5 0.6 100 0.9 0.6807732
3 5 0.6 100 1.0 0.6793675
3 5 0.6 150 0.7 0.6643616
3 5 0.6 150 0.8 0.6757109
3 5 0.6 150 0.9 0.6784040
3 5 0.6 150 1.0 0.6777040
3 5 0.7 50 0.7 0.6869943
3 5 0.7 50 0.8 0.6866683
3 5 0.7 50 0.9 0.6854616
3 5 0.7 50 1.0 0.6834358
3 5 0.7 100 0.7 0.6812971
3 5 0.7 100 0.8 0.6813765
3 5 0.7 100 0.9 0.6806099
3 5 0.7 100 1.0 0.6789488
3 5 0.7 150 0.7 0.6644564
3 5 0.7 150 0.8 0.6752946
3 5 0.7 150 0.9 0.6783465
3 5 0.7 150 1.0 0.6773989
3 5 0.8 50 0.7 0.6867668
3 5 0.8 50 0.8 0.6862030
3 5 0.8 50 0.9 0.6853349
3 5 0.8 50 1.0 0.6830479
3 5 0.8 100 0.7 0.6812639
3 5 0.8 100 0.8 0.6814151
3 5 0.8 100 0.9 0.6803397
3 5 0.8 100 1.0 0.6785948
3 5 0.8 150 0.7 0.6642576
3 5 0.8 150 0.8 0.6758489
3 5 0.8 150 0.9 0.6781679
3 5 0.8 150 1.0 0.6769206
3 5 0.9 50 0.7 0.6867381
3 5 0.9 50 0.8 0.6856327
3 5 0.9 50 0.9 0.6850105
3 5 0.9 50 1.0 0.6827118
3 5 0.9 100 0.7 0.6813817
3 5 0.9 100 0.8 0.6814381
3 5 0.9 100 0.9 0.6800987
3 5 0.9 100 1.0 0.6783352
3 5 0.9 150 0.7 0.6640628
3 5 0.9 150 0.8 0.6753909
3 5 0.9 150 0.9 0.6780008
3 5 0.9 150 1.0 0.6767821
3 5 1.0 50 0.7 0.6864516
3 5 1.0 50 0.8 0.6859168
3 5 1.0 50 0.9 0.6850389
3 5 1.0 50 1.0 0.6827433
3 5 1.0 100 0.7 0.6809974
3 5 1.0 100 0.8 0.6811551
3 5 1.0 100 0.9 0.6802319
3 5 1.0 100 1.0 0.6780346
3 5 1.0 150 0.7 0.6639851
3 5 1.0 150 0.8 0.6751309
3 5 1.0 150 0.9 0.6780554
3 5 1.0 150 1.0 0.6762389
3 10 0.1 50 0.7 0.6809211
3 10 0.1 50 0.8 0.6817948
3 10 0.1 50 0.9 0.6828091
3 10 0.1 50 1.0 0.6832202
3 10 0.1 100 0.7 0.6732088
3 10 0.1 100 0.8 0.6746434
3 10 0.1 100 0.9 0.6762844
3 10 0.1 100 1.0 0.6771452
3 10 0.1 150 0.7 0.6560619
3 10 0.1 150 0.8 0.6671843
3 10 0.1 150 0.9 0.6708748
3 10 0.1 150 1.0 0.6734087
3 10 0.2 50 0.7 0.6844540
3 10 0.2 50 0.8 0.6848539
3 10 0.2 50 0.9 0.6849360
3 10 0.2 50 1.0 0.6846056
3 10 0.2 100 0.7 0.6776530
3 10 0.2 100 0.8 0.6782035
3 10 0.2 100 0.9 0.6787499
3 10 0.2 100 1.0 0.6787681
3 10 0.2 150 0.7 0.6607183
3 10 0.2 150 0.8 0.6712473
3 10 0.2 150 0.9 0.6748159
3 10 0.2 150 1.0 0.6752302
3 10 0.3 50 0.7 0.6859066
3 10 0.3 50 0.8 0.6860234
3 10 0.3 50 0.9 0.6852955
3 10 0.3 50 1.0 0.6847555
3 10 0.3 100 0.7 0.6787642
3 10 0.3 100 0.8 0.6796664
3 10 0.3 100 0.9 0.6794796
3 10 0.3 100 1.0 0.6785490
3 10 0.3 150 0.7 0.6621122
3 10 0.3 150 0.8 0.6731435
3 10 0.3 150 0.9 0.6761060
3 10 0.3 150 1.0 0.6759633
3 10 0.4 50 0.7 0.6864417
3 10 0.4 50 0.8 0.6861661
3 10 0.4 50 0.9 0.6853435
3 10 0.4 50 1.0 0.6837490
3 10 0.4 100 0.7 0.6796200
3 10 0.4 100 0.8 0.6797002
3 10 0.4 100 0.9 0.6794362
3 10 0.4 100 1.0 0.6778294
3 10 0.4 150 0.7 0.6626165
3 10 0.4 150 0.8 0.6736248
3 10 0.4 150 0.9 0.6766646
3 10 0.4 150 1.0 0.6756458
3 10 0.5 50 0.7 0.6861848
3 10 0.5 50 0.8 0.6858424
3 10 0.5 50 0.9 0.6850492
3 10 0.5 50 1.0 0.6828965
3 10 0.5 100 0.7 0.6797119
3 10 0.5 100 0.8 0.6801569
3 10 0.5 100 0.9 0.6795293
3 10 0.5 100 1.0 0.6775871
3 10 0.5 150 0.7 0.6627240
3 10 0.5 150 0.8 0.6740639
3 10 0.5 150 0.9 0.6766608
3 10 0.5 150 1.0 0.6756375
3 10 0.6 50 0.7 0.6860649
3 10 0.6 50 0.8 0.6859989
3 10 0.6 50 0.9 0.6850988
3 10 0.6 50 1.0 0.6819247
3 10 0.6 100 0.7 0.6800209
3 10 0.6 100 0.8 0.6801226
3 10 0.6 100 0.9 0.6795044
3 10 0.6 100 1.0 0.6767139
3 10 0.6 150 0.7 0.6629846
3 10 0.6 150 0.8 0.6739789
3 10 0.6 150 0.9 0.6768929
3 10 0.6 150 1.0 0.6750335
3 10 0.7 50 0.7 0.6867090
3 10 0.7 50 0.8 0.6857587
3 10 0.7 50 0.9 0.6844336
3 10 0.7 50 1.0 0.6815856
3 10 0.7 100 0.7 0.6800509
3 10 0.7 100 0.8 0.6803787
3 10 0.7 100 0.9 0.6794897
3 10 0.7 100 1.0 0.6761563
3 10 0.7 150 0.7 0.6628629
3 10 0.7 150 0.8 0.6745081
3 10 0.7 150 0.9 0.6773100
3 10 0.7 150 1.0 0.6742109
3 10 0.8 50 0.7 0.6865911
3 10 0.8 50 0.8 0.6858295
3 10 0.8 50 0.9 0.6845177
3 10 0.8 50 1.0 0.6812633
3 10 0.8 100 0.7 0.6798889
3 10 0.8 100 0.8 0.6801985
3 10 0.8 100 0.9 0.6791392
3 10 0.8 100 1.0 0.6752740
3 10 0.8 150 0.7 0.6627948
3 10 0.8 150 0.8 0.6741518
3 10 0.8 150 0.9 0.6771134
3 10 0.8 150 1.0 0.6737037
3 10 0.9 50 0.7 0.6860232
3 10 0.9 50 0.8 0.6856790
3 10 0.9 50 0.9 0.6841102
3 10 0.9 50 1.0 0.6806440
3 10 0.9 100 0.7 0.6801130
3 10 0.9 100 0.8 0.6801091
3 10 0.9 100 0.9 0.6792752
3 10 0.9 100 1.0 0.6749215
3 10 0.9 150 0.7 0.6625939
3 10 0.9 150 0.8 0.6741566
3 10 0.9 150 0.9 0.6771434
3 10 0.9 150 1.0 0.6730714
3 10 1.0 50 0.7 0.6862967
3 10 1.0 50 0.8 0.6854939
3 10 1.0 50 0.9 0.6841055
3 10 1.0 50 1.0 0.6793304
3 10 1.0 100 0.7 0.6801912
3 10 1.0 100 0.8 0.6802572
3 10 1.0 100 0.9 0.6792670
3 10 1.0 100 1.0 0.6731540
3 10 1.0 150 0.7 0.6628785
3 10 1.0 150 0.8 0.6742292
3 10 1.0 150 0.9 0.6769259
3 10 1.0 150 1.0 0.6713623
4 0 0.1 50 0.7 0.6847328
4 0 0.1 50 0.8 0.6863620
4 0 0.1 50 0.9 0.6863301
4 0 0.1 50 1.0 0.6878075
4 0 0.1 100 0.7 0.6782228
4 0 0.1 100 0.8 0.6792327
4 0 0.1 100 0.9 0.6809681
4 0 0.1 100 1.0 0.6822375
4 0 0.1 150 0.7 0.6596435
4 0 0.1 150 0.8 0.6703524
4 0 0.1 150 0.9 0.6753260
4 0 0.1 150 1.0 0.6777236
4 0 0.2 50 0.7 0.6875216
4 0 0.2 50 0.8 0.6886783
4 0 0.2 50 0.9 0.6887271
4 0 0.2 50 1.0 0.6883280
4 0 0.2 100 0.7 0.6812408
4 0 0.2 100 0.8 0.6824009
4 0 0.2 100 0.9 0.6831993
4 0 0.2 100 1.0 0.6837253
4 0 0.2 150 0.7 0.6635831
4 0 0.2 150 0.8 0.6748851
4 0 0.2 150 0.9 0.6781447
4 0 0.2 150 1.0 0.6797009
4 0 0.3 50 0.7 0.6884075
4 0 0.3 50 0.8 0.6887481
4 0 0.3 50 0.9 0.6879898
4 0 0.3 50 1.0 0.6877648
4 0 0.3 100 0.7 0.6821881
4 0 0.3 100 0.8 0.6833897
4 0 0.3 100 0.9 0.6833233
4 0 0.3 100 1.0 0.6837386
4 0 0.3 150 0.7 0.6642324
4 0 0.3 150 0.8 0.6759015
4 0 0.3 150 0.9 0.6792247
4 0 0.3 150 1.0 0.6803471
4 0 0.4 50 0.7 0.6883404
4 0 0.4 50 0.8 0.6885086
4 0 0.4 50 0.9 0.6878408
4 0 0.4 50 1.0 0.6869758
4 0 0.4 100 0.7 0.6825654
4 0 0.4 100 0.8 0.6835079
4 0 0.4 100 0.9 0.6836370
4 0 0.4 100 1.0 0.6832321
4 0 0.4 150 0.7 0.6643591
4 0 0.4 150 0.8 0.6760523
4 0 0.4 150 0.9 0.6796202
4 0 0.4 150 1.0 0.6801201
4 0 0.5 50 0.7 0.6888314
4 0 0.5 50 0.8 0.6887450
4 0 0.5 50 0.9 0.6875791
4 0 0.5 50 1.0 0.6858055
4 0 0.5 100 0.7 0.6828824
4 0 0.5 100 0.8 0.6833429
4 0 0.5 100 0.9 0.6830619
4 0 0.5 100 1.0 0.6828737
4 0 0.5 150 0.7 0.6645697
4 0 0.5 150 0.8 0.6761093
4 0 0.5 150 0.9 0.6792369
4 0 0.5 150 1.0 0.6801450
4 0 0.6 50 0.7 0.6879160
4 0 0.6 50 0.8 0.6882025
4 0 0.6 50 0.9 0.6873021
4 0 0.6 50 1.0 0.6853447
4 0 0.6 100 0.7 0.6827113
4 0 0.6 100 0.8 0.6829397
4 0 0.6 100 0.9 0.6827168
4 0 0.6 100 1.0 0.6815674
4 0 0.6 150 0.7 0.6645626
4 0 0.6 150 0.8 0.6761534
4 0 0.6 150 0.9 0.6793968
4 0 0.6 150 1.0 0.6798938
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4 10 1.0 50 0.9 0.6853554
4 10 1.0 50 1.0 0.6799994
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4 10 1.0 100 0.8 0.6811184
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5 5 0.9 50 0.8 0.6873771
5 5 0.9 50 0.9 0.6865987
5 5 0.9 50 1.0 0.6844622
5 5 0.9 100 0.7 0.6826522
5 5 0.9 100 0.8 0.6829828
5 5 0.9 100 0.9 0.6823567
5 5 0.9 100 1.0 0.6806086
5 5 0.9 150 0.7 0.6638731
5 5 0.9 150 0.8 0.6756390
5 5 0.9 150 0.9 0.6790818
5 5 0.9 150 1.0 0.6794999
5 5 1.0 50 0.7 0.6875150
5 5 1.0 50 0.8 0.6873931
5 5 1.0 50 0.9 0.6864322
5 5 1.0 50 1.0 0.6833904
5 5 1.0 100 0.7 0.6823721
5 5 1.0 100 0.8 0.6827442
5 5 1.0 100 0.9 0.6819150
5 5 1.0 100 1.0 0.6797879
5 5 1.0 150 0.7 0.6642695
5 5 1.0 150 0.8 0.6756330
5 5 1.0 150 0.9 0.6787096
5 5 1.0 150 1.0 0.6790400
5 10 0.1 50 0.7 0.6830759
5 10 0.1 50 0.8 0.6842214
5 10 0.1 50 0.9 0.6850778
5 10 0.1 50 1.0 0.6858238
5 10 0.1 100 0.7 0.6728961
5 10 0.1 100 0.8 0.6749677
5 10 0.1 100 0.9 0.6772596
5 10 0.1 100 1.0 0.6792348
5 10 0.1 150 0.7 0.6562027
5 10 0.1 150 0.8 0.6673144
5 10 0.1 150 0.9 0.6712699
5 10 0.1 150 1.0 0.6736586
5 10 0.2 50 0.7 0.6871052
5 10 0.2 50 0.8 0.6874469
5 10 0.2 50 0.9 0.6876994
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5 10 0.2 100 0.7 0.6783674
5 10 0.2 100 0.8 0.6796622
5 10 0.2 100 0.9 0.6805484
5 10 0.2 100 1.0 0.6805603
5 10 0.2 150 0.7 0.6608291
5 10 0.2 150 0.8 0.6715173
5 10 0.2 150 0.9 0.6752548
5 10 0.2 150 1.0 0.6761517
5 10 0.3 50 0.7 0.6883256
5 10 0.3 50 0.8 0.6886301
5 10 0.3 50 0.9 0.6879946
5 10 0.3 50 1.0 0.6874357
5 10 0.3 100 0.7 0.6796422
5 10 0.3 100 0.8 0.6806855
5 10 0.3 100 0.9 0.6810529
5 10 0.3 100 1.0 0.6805164
5 10 0.3 150 0.7 0.6621151
5 10 0.3 150 0.8 0.6731935
5 10 0.3 150 0.9 0.6763354
5 10 0.3 150 1.0 0.6770249
5 10 0.4 50 0.7 0.6885008
5 10 0.4 50 0.8 0.6887145
5 10 0.4 50 0.9 0.6879722
5 10 0.4 50 1.0 0.6867719
5 10 0.4 100 0.7 0.6807627
5 10 0.4 100 0.8 0.6814665
5 10 0.4 100 0.9 0.6811108
5 10 0.4 100 1.0 0.6806590
5 10 0.4 150 0.7 0.6622992
5 10 0.4 150 0.8 0.6737397
5 10 0.4 150 0.9 0.6769721
5 10 0.4 150 1.0 0.6771882
5 10 0.5 50 0.7 0.6885003
5 10 0.5 50 0.8 0.6884834
5 10 0.5 50 0.9 0.6873548
5 10 0.5 50 1.0 0.6864246
5 10 0.5 100 0.7 0.6806654
5 10 0.5 100 0.8 0.6818166
5 10 0.5 100 0.9 0.6813729
5 10 0.5 100 1.0 0.6800700
5 10 0.5 150 0.7 0.6626793
5 10 0.5 150 0.8 0.6738057
5 10 0.5 150 0.9 0.6773062
5 10 0.5 150 1.0 0.6770761
5 10 0.6 50 0.7 0.6880595
5 10 0.6 50 0.8 0.6883155
5 10 0.6 50 0.9 0.6874783
5 10 0.6 50 1.0 0.6854410
5 10 0.6 100 0.7 0.6812213
5 10 0.6 100 0.8 0.6816089
5 10 0.6 100 0.9 0.6813524
5 10 0.6 100 1.0 0.6791986
5 10 0.6 150 0.7 0.6626594
5 10 0.6 150 0.8 0.6744260
5 10 0.6 150 0.9 0.6775530
5 10 0.6 150 1.0 0.6763451
5 10 0.7 50 0.7 0.6885576
5 10 0.7 50 0.8 0.6882475
5 10 0.7 50 0.9 0.6867469
5 10 0.7 50 1.0 0.6847096
5 10 0.7 100 0.7 0.6813189
5 10 0.7 100 0.8 0.6816190
5 10 0.7 100 0.9 0.6811372
5 10 0.7 100 1.0 0.6785478
5 10 0.7 150 0.7 0.6626962
5 10 0.7 150 0.8 0.6746201
5 10 0.7 150 0.9 0.6775022
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5 10 0.8 50 0.7 0.6878269
5 10 0.8 50 0.8 0.6873863
5 10 0.8 50 0.9 0.6868675
5 10 0.8 50 1.0 0.6838779
5 10 0.8 100 0.7 0.6813075
5 10 0.8 100 0.8 0.6817777
5 10 0.8 100 0.9 0.6810258
5 10 0.8 100 1.0 0.6774409
5 10 0.8 150 0.7 0.6628720
5 10 0.8 150 0.8 0.6746112
5 10 0.8 150 0.9 0.6776292
5 10 0.8 150 1.0 0.6750902
5 10 0.9 50 0.7 0.6880005
5 10 0.9 50 0.8 0.6873198
5 10 0.9 50 0.9 0.6862864
5 10 0.9 50 1.0 0.6831184
5 10 0.9 100 0.7 0.6814040
5 10 0.9 100 0.8 0.6816202
5 10 0.9 100 0.9 0.6809315
5 10 0.9 100 1.0 0.6758562
5 10 0.9 150 0.7 0.6630242
5 10 0.9 150 0.8 0.6741520
5 10 0.9 150 0.9 0.6776491
5 10 0.9 150 1.0 0.6742595
5 10 1.0 50 0.7 0.6875712
5 10 1.0 50 0.8 0.6872827
5 10 1.0 50 0.9 0.6859752
5 10 1.0 50 1.0 0.6800202
5 10 1.0 100 0.7 0.6816957
5 10 1.0 100 0.8 0.6818457
5 10 1.0 100 0.9 0.6806862
5 10 1.0 100 1.0 0.6731161
5 10 1.0 150 0.7 0.6629683
5 10 1.0 150 0.8 0.6748128
5 10 1.0 150 0.9 0.6777434
5 10 1.0 150 1.0 0.6722023
6 0 0.1 50 0.7 0.6852237
6 0 0.1 50 0.8 0.6860644
6 0 0.1 50 0.9 0.6870480
6 0 0.1 50 1.0 0.6879913
6 0 0.1 100 0.7 0.6778570
6 0 0.1 100 0.8 0.6796859
6 0 0.1 100 0.9 0.6811659
6 0 0.1 100 1.0 0.6827180
6 0 0.1 150 0.7 0.6596584
6 0 0.1 150 0.8 0.6705130
6 0 0.1 150 0.9 0.6756121
6 0 0.1 150 1.0 0.6772374
6 0 0.2 50 0.7 0.6886852
6 0 0.2 50 0.8 0.6892757
6 0 0.2 50 0.9 0.6894854
6 0 0.2 50 1.0 0.6897523
6 0 0.2 100 0.7 0.6815605
6 0 0.2 100 0.8 0.6825122
6 0 0.2 100 0.9 0.6839066
6 0 0.2 100 1.0 0.6841694
6 0 0.2 150 0.7 0.6638156
6 0 0.2 150 0.8 0.6747548
6 0 0.2 150 0.9 0.6785134
6 0 0.2 150 1.0 0.6802466
6 0 0.3 50 0.7 0.6891782
6 0 0.3 50 0.8 0.6895616
6 0 0.3 50 0.9 0.6890935
6 0 0.3 50 1.0 0.6889510
6 0 0.3 100 0.7 0.6823972
6 0 0.3 100 0.8 0.6831671
6 0 0.3 100 0.9 0.6841509
6 0 0.3 100 1.0 0.6847003
6 0 0.3 150 0.7 0.6643410
6 0 0.3 150 0.8 0.6756683
6 0 0.3 150 0.9 0.6794086
6 0 0.3 150 1.0 0.6803306
6 0 0.4 50 0.7 0.6889784
6 0 0.4 50 0.8 0.6893509
6 0 0.4 50 0.9 0.6885060
6 0 0.4 50 1.0 0.6880380
6 0 0.4 100 0.7 0.6826760
6 0 0.4 100 0.8 0.6837345
6 0 0.4 100 0.9 0.6838489
6 0 0.4 100 1.0 0.6839660
6 0 0.4 150 0.7 0.6645391
6 0 0.4 150 0.8 0.6756803
6 0 0.4 150 0.9 0.6796747
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6 0 0.5 50 0.7 0.6889983
6 0 0.5 50 0.8 0.6889320
6 0 0.5 50 0.9 0.6881294
6 0 0.5 50 1.0 0.6868902
6 0 0.5 100 0.7 0.6829666
6 0 0.5 100 0.8 0.6835853
6 0 0.5 100 0.9 0.6838545
6 0 0.5 100 1.0 0.6836335
6 0 0.5 150 0.7 0.6642064
6 0 0.5 150 0.8 0.6763404
6 0 0.5 150 0.9 0.6796242
6 0 0.5 150 1.0 0.6804341
6 0 0.6 50 0.7 0.6884327
6 0 0.6 50 0.8 0.6885274
6 0 0.6 50 0.9 0.6871346
6 0 0.6 50 1.0 0.6863454
6 0 0.6 100 0.7 0.6824297
6 0 0.6 100 0.8 0.6836623
6 0 0.6 100 0.9 0.6834332
6 0 0.6 100 1.0 0.6828375
6 0 0.6 150 0.7 0.6643093
6 0 0.6 150 0.8 0.6761066
6 0 0.6 150 0.9 0.6793507
6 0 0.6 150 1.0 0.6801806
6 0 0.7 50 0.7 0.6883989
6 0 0.7 50 0.8 0.6882370
6 0 0.7 50 0.9 0.6869802
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6 0 0.7 100 0.7 0.6828477
6 0 0.7 100 0.8 0.6834253
6 0 0.7 100 0.9 0.6829988
6 0 0.7 100 1.0 0.6823374
6 0 0.7 150 0.7 0.6643265
6 0 0.7 150 0.8 0.6760084
6 0 0.7 150 0.9 0.6789969
6 0 0.7 150 1.0 0.6799018
6 0 0.8 50 0.7 0.6879146
6 0 0.8 50 0.8 0.6876267
6 0 0.8 50 0.9 0.6865183
6 0 0.8 50 1.0 0.6849115
6 0 0.8 100 0.7 0.6824920
6 0 0.8 100 0.8 0.6831308
6 0 0.8 100 0.9 0.6827089
6 0 0.8 100 1.0 0.6813424
6 0 0.8 150 0.7 0.6643654
6 0 0.8 150 0.8 0.6757911
6 0 0.8 150 0.9 0.6790753
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6 0 0.9 50 0.7 0.6877505
6 0 0.9 50 0.8 0.6873619
6 0 0.9 50 0.9 0.6863680
6 0 0.9 50 1.0 0.6838402
6 0 0.9 100 0.7 0.6827549
6 0 0.9 100 0.8 0.6828564
6 0 0.9 100 0.9 0.6824288
6 0 0.9 100 1.0 0.6807378
6 0 0.9 150 0.7 0.6642855
6 0 0.9 150 0.8 0.6754859
6 0 0.9 150 0.9 0.6788618
6 0 0.9 150 1.0 0.6795545
6 0 1.0 50 0.7 0.6875813
6 0 1.0 50 0.8 0.6870363
6 0 1.0 50 0.9 0.6860476
6 0 1.0 50 1.0 0.6830631
6 0 1.0 100 0.7 0.6824347
6 0 1.0 100 0.8 0.6827276
6 0 1.0 100 0.9 0.6822599
6 0 1.0 100 1.0 0.6800482
6 0 1.0 150 0.7 0.6639356
6 0 1.0 150 0.8 0.6754080
6 0 1.0 150 0.9 0.6787186
6 0 1.0 150 1.0 0.6790936
6 5 0.1 50 0.7 0.6853063
6 5 0.1 50 0.8 0.6862371
6 5 0.1 50 0.9 0.6875069
6 5 0.1 50 1.0 0.6879292
6 5 0.1 100 0.7 0.6769164
6 5 0.1 100 0.8 0.6788239
6 5 0.1 100 0.9 0.6796957
6 5 0.1 100 1.0 0.6817503
6 5 0.1 150 0.7 0.6593284
6 5 0.1 150 0.8 0.6702711
6 5 0.1 150 0.9 0.6741924
6 5 0.1 150 1.0 0.6765716
6 5 0.2 50 0.7 0.6892679
6 5 0.2 50 0.8 0.6895106
6 5 0.2 50 0.9 0.6897426
6 5 0.2 50 1.0 0.6900170
6 5 0.2 100 0.7 0.6812197
6 5 0.2 100 0.8 0.6820481
6 5 0.2 100 0.9 0.6831950
6 5 0.2 100 1.0 0.6840927
6 5 0.2 150 0.7 0.6629278
6 5 0.2 150 0.8 0.6744425
6 5 0.2 150 0.9 0.6780884
6 5 0.2 150 1.0 0.6799676
6 5 0.3 50 0.7 0.6893518
6 5 0.3 50 0.8 0.6896708
6 5 0.3 50 0.9 0.6897185
6 5 0.3 50 1.0 0.6897346
6 5 0.3 100 0.7 0.6822264
6 5 0.3 100 0.8 0.6836641
6 5 0.3 100 0.9 0.6835633
6 5 0.3 100 1.0 0.6841002
6 5 0.3 150 0.7 0.6641006
6 5 0.3 150 0.8 0.6754012
6 5 0.3 150 0.9 0.6790903
6 5 0.3 150 1.0 0.6799906
6 5 0.4 50 0.7 0.6892595
6 5 0.4 50 0.8 0.6895310
6 5 0.4 50 0.9 0.6891521
6 5 0.4 50 1.0 0.6886840
6 5 0.4 100 0.7 0.6824526
6 5 0.4 100 0.8 0.6835653
6 5 0.4 100 0.9 0.6837550
6 5 0.4 100 1.0 0.6838585
6 5 0.4 150 0.7 0.6645968
6 5 0.4 150 0.8 0.6757349
6 5 0.4 150 0.9 0.6796041
6 5 0.4 150 1.0 0.6802671
6 5 0.5 50 0.7 0.6888390
6 5 0.5 50 0.8 0.6889029
6 5 0.5 50 0.9 0.6890214
6 5 0.5 50 1.0 0.6875298
6 5 0.5 100 0.7 0.6824863
6 5 0.5 100 0.8 0.6835458
6 5 0.5 100 0.9 0.6841375
6 5 0.5 100 1.0 0.6830824
6 5 0.5 150 0.7 0.6644730
6 5 0.5 150 0.8 0.6762431
6 5 0.5 150 0.9 0.6795053
6 5 0.5 150 1.0 0.6803461
6 5 0.6 50 0.7 0.6886507
6 5 0.6 50 0.8 0.6886590
6 5 0.6 50 0.9 0.6881100
6 5 0.6 50 1.0 0.6864654
6 5 0.6 100 0.7 0.6824208
6 5 0.6 100 0.8 0.6835713
6 5 0.6 100 0.9 0.6832954
6 5 0.6 100 1.0 0.6825258
6 5 0.6 150 0.7 0.6645368
6 5 0.6 150 0.8 0.6760907
6 5 0.6 150 0.9 0.6792732
6 5 0.6 150 1.0 0.6800751
6 5 0.7 50 0.7 0.6885931
6 5 0.7 50 0.8 0.6882508
6 5 0.7 50 0.9 0.6868195
6 5 0.7 50 1.0 0.6855094
6 5 0.7 100 0.7 0.6822280
6 5 0.7 100 0.8 0.6835859
6 5 0.7 100 0.9 0.6830010
6 5 0.7 100 1.0 0.6819696
6 5 0.7 150 0.7 0.6645558
6 5 0.7 150 0.8 0.6759282
6 5 0.7 150 0.9 0.6792275
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6 5 0.8 50 0.7 0.6881431
6 5 0.8 50 0.8 0.6877723
6 5 0.8 50 0.9 0.6869730
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6 5 0.8 100 0.7 0.6826415
6 5 0.8 100 0.8 0.6831026
6 5 0.8 100 0.9 0.6827019
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6 5 0.8 150 0.8 0.6758811
6 5 0.8 150 0.9 0.6791805
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6 5 0.9 50 0.7 0.6877357
6 5 0.9 50 0.8 0.6870044
6 5 0.9 50 0.9 0.6868449
6 5 0.9 50 1.0 0.6843470
6 5 0.9 100 0.7 0.6827196
6 5 0.9 100 0.8 0.6831285
6 5 0.9 100 0.9 0.6826469
6 5 0.9 100 1.0 0.6807128
6 5 0.9 150 0.7 0.6642852
6 5 0.9 150 0.8 0.6756329
6 5 0.9 150 0.9 0.6787216
6 5 0.9 150 1.0 0.6794853
6 5 1.0 50 0.7 0.6875976
6 5 1.0 50 0.8 0.6869853
6 5 1.0 50 0.9 0.6857785
6 5 1.0 50 1.0 0.6833452
6 5 1.0 100 0.7 0.6824841
6 5 1.0 100 0.8 0.6827792
6 5 1.0 100 0.9 0.6821465
6 5 1.0 100 1.0 0.6802791
6 5 1.0 150 0.7 0.6639853
6 5 1.0 150 0.8 0.6755159
6 5 1.0 150 0.9 0.6789531
6 5 1.0 150 1.0 0.6790041
6 10 0.1 50 0.7 0.6833072
6 10 0.1 50 0.8 0.6841575
6 10 0.1 50 0.9 0.6854969
6 10 0.1 50 1.0 0.6868778
6 10 0.1 100 0.7 0.6738920
6 10 0.1 100 0.8 0.6756873
6 10 0.1 100 0.9 0.6770569
6 10 0.1 100 1.0 0.6789594
6 10 0.1 150 0.7 0.6565247
6 10 0.1 150 0.8 0.6673347
6 10 0.1 150 0.9 0.6714006
6 10 0.1 150 1.0 0.6736297
6 10 0.2 50 0.7 0.6869796
6 10 0.2 50 0.8 0.6884403
6 10 0.2 50 0.9 0.6882535
6 10 0.2 50 1.0 0.6886576
6 10 0.2 100 0.7 0.6780076
6 10 0.2 100 0.8 0.6793125
6 10 0.2 100 0.9 0.6803871
6 10 0.2 100 1.0 0.6809719
6 10 0.2 150 0.7 0.6610053
6 10 0.2 150 0.8 0.6718623
6 10 0.2 150 0.9 0.6754911
6 10 0.2 150 1.0 0.6761567
6 10 0.3 50 0.7 0.6881619
6 10 0.3 50 0.8 0.6887839
6 10 0.3 50 0.9 0.6884519
6 10 0.3 50 1.0 0.6880864
6 10 0.3 100 0.7 0.6799895
6 10 0.3 100 0.8 0.6806928
6 10 0.3 100 0.9 0.6812943
6 10 0.3 100 1.0 0.6809276
6 10 0.3 150 0.7 0.6619809
6 10 0.3 150 0.8 0.6732694
6 10 0.3 150 0.9 0.6765121
6 10 0.3 150 1.0 0.6771486
6 10 0.4 50 0.7 0.6884591
6 10 0.4 50 0.8 0.6887332
6 10 0.4 50 0.9 0.6883677
6 10 0.4 50 1.0 0.6879674
6 10 0.4 100 0.7 0.6806380
6 10 0.4 100 0.8 0.6812785
6 10 0.4 100 0.9 0.6816668
6 10 0.4 100 1.0 0.6808185
6 10 0.4 150 0.7 0.6625136
6 10 0.4 150 0.8 0.6738479
6 10 0.4 150 0.9 0.6769585
6 10 0.4 150 1.0 0.6772111
6 10 0.5 50 0.7 0.6888950
6 10 0.5 50 0.8 0.6891724
6 10 0.5 50 0.9 0.6881573
6 10 0.5 50 1.0 0.6869669
6 10 0.5 100 0.7 0.6807297
6 10 0.5 100 0.8 0.6814664
6 10 0.5 100 0.9 0.6816429
6 10 0.5 100 1.0 0.6800098
6 10 0.5 150 0.7 0.6626449
6 10 0.5 150 0.8 0.6743985
6 10 0.5 150 0.9 0.6773441
6 10 0.5 150 1.0 0.6768153
6 10 0.6 50 0.7 0.6887057
6 10 0.6 50 0.8 0.6884648
6 10 0.6 50 0.9 0.6877511
6 10 0.6 50 1.0 0.6859362
6 10 0.6 100 0.7 0.6810587
6 10 0.6 100 0.8 0.6816375
6 10 0.6 100 0.9 0.6814926
6 10 0.6 100 1.0 0.6793078
6 10 0.6 150 0.7 0.6628377
6 10 0.6 150 0.8 0.6742564
6 10 0.6 150 0.9 0.6776723
6 10 0.6 150 1.0 0.6765123
6 10 0.7 50 0.7 0.6884675
6 10 0.7 50 0.8 0.6885591
6 10 0.7 50 0.9 0.6870946
6 10 0.7 50 1.0 0.6858389
6 10 0.7 100 0.7 0.6812138
6 10 0.7 100 0.8 0.6819030
6 10 0.7 100 0.9 0.6810909
6 10 0.7 100 1.0 0.6786860
6 10 0.7 150 0.7 0.6625959
6 10 0.7 150 0.8 0.6743257
6 10 0.7 150 0.9 0.6774460
6 10 0.7 150 1.0 0.6758112
6 10 0.8 50 0.7 0.6885911
6 10 0.8 50 0.8 0.6875500
6 10 0.8 50 0.9 0.6866617
6 10 0.8 50 1.0 0.6845842
6 10 0.8 100 0.7 0.6814665
6 10 0.8 100 0.8 0.6813680
6 10 0.8 100 0.9 0.6813611
6 10 0.8 100 1.0 0.6773865
6 10 0.8 150 0.7 0.6629286
6 10 0.8 150 0.8 0.6744630
6 10 0.8 150 0.9 0.6776145
6 10 0.8 150 1.0 0.6749504
6 10 0.9 50 0.7 0.6880842
6 10 0.9 50 0.8 0.6873045
6 10 0.9 50 0.9 0.6870169
6 10 0.9 50 1.0 0.6837731
6 10 0.9 100 0.7 0.6813271
6 10 0.9 100 0.8 0.6817929
6 10 0.9 100 0.9 0.6811357
6 10 0.9 100 1.0 0.6760899
6 10 0.9 150 0.7 0.6628098
6 10 0.9 150 0.8 0.6746418
6 10 0.9 150 0.9 0.6771468
6 10 0.9 150 1.0 0.6738972
6 10 1.0 50 0.7 0.6881880
6 10 1.0 50 0.8 0.6873952
6 10 1.0 50 0.9 0.6860298
6 10 1.0 50 1.0 0.6806109
6 10 1.0 100 0.7 0.6813693
6 10 1.0 100 0.8 0.6817042
6 10 1.0 100 0.9 0.6808658
6 10 1.0 100 1.0 0.6730474
6 10 1.0 150 0.7 0.6627936
6 10 1.0 150 0.8 0.6745829
6 10 1.0 150 0.9 0.6773657
6 10 1.0 150 1.0 0.6721442
Sens Spec
0.5675303 0.6891103
0.5702681 0.6908684
0.5669041 0.6911055
0.5683156 0.6914432
0.5788793 0.6730122
0.5809902 0.6732140
0.5769985 0.6756490
0.5758241 0.6785242
0.5625153 0.6698323
0.5717515 0.6732451
0.5816164 0.6693245
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0.6003186 0.6527535
0.5928894 0.6766982
0.5908520 0.6762922
0.5925735 0.6780855
0.5903012 0.6748391
0.5971115 0.6619529
0.5989112 0.6602291
0.5978171 0.6606343
0.5961740 0.6608710
0.5827898 0.6476792
0.5929632 0.6519743
0.6025126 0.6523135
0.6035282 0.6501153
0.5906183 0.6776111
0.5896017 0.6756494
0.5928088 0.6762248
0.5888943 0.6746703
0.5978983 0.6603976
0.6001639 0.6612094
0.6011814 0.6600254
0.5958612 0.6583345
0.5825518 0.6463260
0.5980481 0.6502496
0.6053263 0.6522123
0.6057203 0.6477828
0.5910092 0.6767662
0.5921808 0.6769358
0.5893615 0.6782886
0.5889755 0.6763269
0.6002420 0.6593487
0.6024332 0.6613438
0.5990723 0.6604313
0.5956225 0.6591463
0.5849779 0.6447705
0.5984409 0.6500807
0.6057191 0.6499460
0.6055631 0.6476814
0.5922583 0.6773412
0.5909283 0.6769349
0.5903036 0.6782886
0.5880288 0.6751784
0.6012577 0.6599571
0.6007917 0.6605660
0.6004804 0.6602619
0.5938260 0.6584018
0.5862295 0.6440607
0.6004730 0.6488975
0.6080680 0.6506558
0.6059556 0.6455491
0.5913940 0.6761907
0.5895230 0.6761578
0.5927286 0.6790994
0.5862298 0.6749081
0.6013339 0.6585363
0.6021982 0.6616140
0.6017325 0.6602972
0.5931164 0.6575225
0.5870147 0.6436882
0.5994605 0.6479837
0.6069691 0.6480521
0.6054850 0.6438587
0.5924942 0.6777468
0.5907745 0.6779154
0.5882644 0.6781521
0.5810631 0.6720666
0.6029786 0.6587733
0.6025938 0.6616144
0.5990699 0.6605658
0.5908496 0.6546141
0.5872503 0.6446697
0.6003183 0.6488626
0.6077552 0.6475106
0.6053300 0.6428105
Tuning parameter 'nrounds' was held constant at a value of 500
Tuning
parameter 'eta' was held constant at a value of 0.01
Sens was used to select the optimal model using the largest value.
The final values used for the model were nrounds = 500, max_depth = 5, eta
= 0.01, gamma = 5, colsample_bytree = 0.8, min_child_weight = 150
and subsample = 0.9.
Support Vector Machines with Radial Basis Function Kernel
4235 samples
91 predictor
2 classes: 'Not_D_and_inf_Q1', 'Not_D_and_sup_Q1'
Pre-processing: centered (175), scaled (175)
Resampling: Cross-Validated (5 fold, repeated 10 times)
Summary of sample sizes: 3388, 3389, 3388, 3388, 3387, 3389, ...
Resampling results across tuning parameters:
C ROC Sens Spec
2.500000e-01 0.6976585 0.23568689 0.9250566
5.000000e-01 0.6973380 0.22485338 0.9307402
1.000000e+00 0.6975264 0.21299816 0.9370956
2.000000e+00 0.6938192 0.20924694 0.9383481
4.000000e+00 0.6837066 0.18584804 0.9414268
8.000000e+00 0.6684536 0.14610447 0.9515398
1.600000e+01 0.6532007 0.11949387 0.9588425
3.200000e+01 0.6438928 0.10642632 0.9612122
6.400000e+01 0.6400056 0.09968150 0.9635761
1.280000e+02 0.6401726 0.09626960 0.9637523
2.560000e+02 0.6393962 0.09887217 0.9617438
5.120000e+02 0.6397673 0.09867310 0.9626306
1.024000e+03 0.6388583 0.09789401 0.9610761
2.048000e+03 0.6389637 0.09914705 0.9618285
4.096000e+03 0.6383471 0.09712576 0.9620660
8.192000e+03 0.6391102 0.09780050 0.9622009
1.638400e+04 0.6395386 0.10052115 0.9606940
3.276800e+04 0.6385575 0.10163467 0.9611850
6.553600e+04 0.6394898 0.10146635 0.9611069
1.310720e+05 0.6398151 0.09914085 0.9622236
2.621440e+05 0.6396583 0.09724390 0.9628375
5.242880e+05 0.6396754 0.10123581 0.9610416
1.048576e+06 0.6389994 0.09937321 0.9619547
2.097152e+06 0.6389813 0.10317928 0.9607785
4.194304e+06 0.6386431 0.09555271 0.9636617
8.388608e+06 0.6396888 0.09813336 0.9629064
1.677722e+07 0.6391124 0.09523688 0.9635908
3.355443e+07 0.6387180 0.09814112 0.9616982
6.710886e+07 0.6390080 0.10098691 0.9606777
1.342177e+08 0.6391311 0.09681503 0.9621168
2.684355e+08 0.6394517 0.10155969 0.9612812
5.368709e+08 0.6394638 0.09765062 0.9630412
1.073742e+09 0.6382461 0.09824635 0.9618810
2.147484e+09 0.6391314 0.10172694 0.9601087
4.294967e+09 0.6396860 0.09493297 0.9639404
8.589935e+09 0.6392792 0.10014276 0.9613966
1.717987e+10 0.6392087 0.09681660 0.9625185
3.435974e+10 0.6398650 0.09900054 0.9625913
6.871948e+10 0.6392076 0.09806040 0.9625479
1.374390e+11 0.6395644 0.09805358 0.9626950
2.748779e+11 0.6397673 0.09733915 0.9623613
5.497558e+11 0.6394109 0.09669649 0.9629723
1.099512e+12 0.6393373 0.09772893 0.9627284
2.199023e+12 0.6391587 0.09606898 0.9632310
4.398047e+12 0.6394705 0.09908807 0.9622501
8.796093e+12 0.6395876 0.09692783 0.9624195
1.759219e+13 0.6385105 0.10364877 0.9601750
3.518437e+13 0.6397214 0.09724296 0.9630430
7.036874e+13 0.6386202 0.10462656 0.9587578
1.407375e+14 0.6392347 0.09529080 0.9641021
2.814750e+14 0.6387542 0.09781933 0.9620619
5.629500e+14 0.6394705 0.09805141 0.9626978
1.125900e+15 0.6391255 0.09858303 0.9616619
2.251800e+15 0.6392809 0.09912429 0.9620261
4.503600e+15 0.6391806 0.09765114 0.9632577
9.007199e+15 0.6395730 0.10024223 0.9619560
1.801440e+16 0.6383906 0.09939149 0.9619743
3.602880e+16 0.6402438 0.09901398 0.9617987
7.205759e+16 0.6379828 0.09755041 0.9631075
1.441152e+17 0.6397673 0.09945006 0.9618536
2.882304e+17 0.6389456 0.10279940 0.9602450
5.764608e+17 0.6397673 0.09695006 0.9637792
1.152922e+18 0.6383644 0.10242168 0.9597471
2.305843e+18 0.6394220 0.09708244 0.9623413
4.611686e+18 0.6394852 0.10020821 0.9619044
9.223372e+18 0.6391314 0.09933442 0.9618008
1.844674e+19 0.6394220 0.09406786 0.9641409
3.689349e+19 0.6397673 0.09953646 0.9617519
7.378698e+19 0.6391088 0.09921354 0.9618646
1.475740e+20 0.6397673 0.09819914 0.9623944
2.951479e+20 0.6396399 0.09597527 0.9625574
5.902958e+20 0.6397673 0.09789154 0.9623611
1.180592e+21 0.6393350 0.09610779 0.9629752
2.361183e+21 0.6395663 0.09581182 0.9639132
4.722366e+21 0.6391314 0.09949449 0.9611777
9.444733e+21 0.6391571 0.10098741 0.9616727
1.888947e+22 0.6397673 0.09655607 0.9640853
3.777893e+22 0.6389151 0.09900129 0.9616578
7.555786e+22 0.6396860 0.09964017 0.9623510
1.511157e+23 0.6386795 0.10019481 0.9618830
3.022315e+23 0.6387433 0.09532749 0.9632306
6.044629e+23 0.6396860 0.09948167 0.9624911
1.208926e+24 0.6389956 0.09805549 0.9628015
2.417852e+24 0.6391314 0.10021540 0.9611786
4.835703e+24 0.6388345 0.10396785 0.9596337
9.671407e+24 0.6393219 0.09789082 0.9626587
1.934281e+25 0.6386196 0.09765026 0.9624322
3.868563e+25 0.6396583 0.09948292 0.9613855
7.737125e+25 0.6397673 0.09764737 0.9632400
1.547425e+26 0.6397673 0.09695343 0.9630378
3.094850e+26 0.6392557 0.09871866 0.9623776
6.189700e+26 0.6391314 0.10005190 0.9612809
1.237940e+27 0.6392780 0.10089207 0.9619025
2.475880e+27 0.6394572 0.09716478 0.9629400
4.951760e+27 0.6388834 0.10146081 0.9617550
9.903520e+27 0.6395960 0.09872171 0.9625140
1.980704e+28 0.6386008 0.10233762 0.9594935
3.961408e+28 0.6396583 0.09620942 0.9628697
7.922816e+28 0.6397673 0.09827635 0.9623265
1.584563e+29 0.6391550 0.10080341 0.9616482
Tuning parameter 'sigma' was held constant at a value of 0.003586236
ROC was used to select the optimal model using the largest value.
The final values used for the model were sigma = 0.003586236 and C = 0.25.
glmnet
4235 samples
91 predictor
2 classes: 'Not_D_and_inf_Q1', 'Not_D_and_sup_Q1'
Pre-processing: centered (175), scaled (175)
Resampling: Cross-Validated (5 fold, repeated 10 times)
Summary of sample sizes: 3389, 3388, 3387, 3387, 3389, 3388, ...
Resampling results across tuning parameters:
alpha lambda ROC Sens Spec
0.1000000 0.0001105891 0.6969958 0.3165223652 0.8899204
0.1000000 0.0001211481 0.6970216 0.3164442402 0.8900218
0.1000000 0.0001327151 0.6970529 0.3164442402 0.8900218
0.1000000 0.0001453866 0.6970903 0.3163661152 0.8901234
0.1000000 0.0001592679 0.6971215 0.3164442402 0.8900895
0.1000000 0.0001744746 0.6971644 0.3163664216 0.8902249
0.1000000 0.0001911333 0.6972133 0.3160533088 0.8902924
0.1000000 0.0002093824 0.6972778 0.3159751838 0.8903262
0.1000000 0.0002293740 0.6973534 0.3161320466 0.8903939
0.1000000 0.0002512743 0.6974227 0.3159757966 0.8904615
0.1000000 0.0002752657 0.6974937 0.3158192402 0.8905293
0.1000000 0.0003015477 0.6975919 0.3153498775 0.8904955
0.1000000 0.0003303391 0.6976847 0.3151151961 0.8906984
0.1000000 0.0003618795 0.6977852 0.3149586397 0.8907998
0.1000000 0.0003964313 0.6978864 0.3151933211 0.8910028
0.1000000 0.0004342821 0.6979925 0.3149580270 0.8911720
0.1000000 0.0004757468 0.6981003 0.3147236520 0.8913072
0.1000000 0.0005211705 0.6982306 0.3148020833 0.8915439
0.1000000 0.0005709312 0.6983526 0.3147236520 0.8916792
0.1000000 0.0006254430 0.6984726 0.3146458333 0.8918145
0.1000000 0.0006851595 0.6985987 0.3144108456 0.8921188
0.1000000 0.0007505777 0.6987402 0.3140977328 0.8922541
0.1000000 0.0008222419 0.6988770 0.3138630515 0.8923217
0.1000000 0.0009007486 0.6990118 0.3135502451 0.8924572
0.1000000 0.0009867509 0.6991633 0.3128468137 0.8927279
0.1000000 0.0010809647 0.6993308 0.3128471201 0.8928631
0.1000000 0.0011841739 0.6994848 0.3119865196 0.8930998
0.1000000 0.0012972373 0.6996625 0.3112037377 0.8933027
0.1000000 0.0014210959 0.6998306 0.3106559436 0.8935392
0.1000000 0.0015567804 0.7000065 0.3106553309 0.8939113
0.1000000 0.0017054198 0.7001801 0.3105772059 0.8940128
0.1000000 0.0018682512 0.7003766 0.3095597426 0.8943510
0.1000000 0.0020466295 0.7005811 0.3087766544 0.8946213
0.1000000 0.0022420392 0.7007567 0.3083075980 0.8948243
0.1000000 0.0024561063 0.7009465 0.3076819853 0.8952977
0.1000000 0.0026906122 0.7011374 0.3065854779 0.8957374
0.1000000 0.0029475086 0.7013579 0.3059601716 0.8962108
0.1000000 0.0032289330 0.7015703 0.3052552083 0.8967179
0.1000000 0.0035372275 0.7017802 0.3047064951 0.8973603
0.1000000 0.0038749576 0.7020185 0.3032981005 0.8976648
0.1000000 0.0042449337 0.7022563 0.3019672181 0.8982737
0.1000000 0.0046502347 0.7025167 0.2995410539 0.8994236
0.1000000 0.0050942334 0.7027591 0.2980514706 0.9003369
0.1000000 0.0055806244 0.7029947 0.2965640319 0.9010811
0.1000000 0.0061134555 0.7032462 0.2944515931 0.9018590
0.1000000 0.0066971607 0.7034423 0.2928082108 0.9029077
0.1000000 0.0073365972 0.7036901 0.2902267157 0.9040918
0.1000000 0.0080370864 0.7038957 0.2876430760 0.9050047
0.1000000 0.0088044575 0.7041165 0.2852172181 0.9060868
0.1000000 0.0096450961 0.7043364 0.2833391544 0.9069659
0.1000000 0.0105659979 0.7045807 0.2802861520 0.9086232
0.1000000 0.0115748263 0.7047671 0.2780955882 0.9104158
0.1000000 0.0126799763 0.7050107 0.2747310049 0.9118699
0.1000000 0.0138906447 0.7051819 0.2723051471 0.9134928
0.1000000 0.0152169061 0.7053652 0.2690165441 0.9158938
0.1000000 0.0166697974 0.7055331 0.2651066176 0.9173481
0.1000000 0.0182614090 0.7056203 0.2615067402 0.9189373
0.1000000 0.0200049856 0.7056732 0.2571243873 0.9206619
0.1000000 0.0219150368 0.7057318 0.2538376225 0.9226575
0.1000000 0.0240074572 0.7057790 0.2503943015 0.9246526
0.1000000 0.0262996594 0.7057979 0.2460107230 0.9273245
0.1000000 0.0288107182 0.7057642 0.2405318627 0.9302663
0.1000000 0.0315615298 0.7056476 0.2347408088 0.9327688
0.1000000 0.0345749854 0.7055125 0.2279347426 0.9353055
0.1000000 0.0378761620 0.7053204 0.2211268382 0.9377409
0.1000000 0.0414925309 0.7051078 0.2143958333 0.9406492
0.1000000 0.0454541862 0.7047560 0.2059451593 0.9436925
0.1000000 0.0497940954 0.7043484 0.1974160539 0.9477845
0.1000000 0.0545483737 0.7039209 0.1902962623 0.9510651
0.1000000 0.0597565847 0.7032435 0.1812981005 0.9548192
0.1000000 0.0654620691 0.7023703 0.1731605392 0.9579302
0.1000000 0.0717123061 0.7013023 0.1633792892 0.9613125
0.1000000 0.0785593079 0.7000142 0.1546917892 0.9641868
0.1000000 0.0860600530 0.6984931 0.1457726716 0.9669597
0.1000000 0.0942769599 0.6968132 0.1368523284 0.9706461
0.1000000 0.1032784069 0.6949324 0.1274613971 0.9727428
0.1000000 0.1131393008 0.6928685 0.1197935049 0.9752114
0.1000000 0.1239417006 0.6906830 0.1090741422 0.9778494
0.1000000 0.1357755002 0.6885195 0.0991354167 0.9803179
0.1000000 0.1487391762 0.6860959 0.0867732843 0.9831588
0.1090909 0.0001105891 0.6969885 0.3165226716 0.8901571
0.1090909 0.0001211481 0.6970211 0.3165226716 0.8902586
0.1090909 0.0001327151 0.6970574 0.3164445466 0.8902586
0.1090909 0.0001453866 0.6970934 0.3163661152 0.8902586
0.1090909 0.0001592679 0.6971332 0.3163661152 0.8902586
0.1090909 0.0001744746 0.6971798 0.3162098652 0.8903262
0.1090909 0.0001911333 0.6972402 0.3161314338 0.8903600
0.1090909 0.0002093824 0.6973208 0.3158970588 0.8904276
0.1090909 0.0002293740 0.6973990 0.3159757966 0.8904615
0.1090909 0.0002512743 0.6974717 0.3158976716 0.8904953
0.1090909 0.0002752657 0.6975491 0.3158189338 0.8905631
0.1090909 0.0003015477 0.6976274 0.3153498775 0.8905630
0.1090909 0.0003303391 0.6977315 0.3150370711 0.8907659
0.1090909 0.0003618795 0.6978150 0.3151936275 0.8909351
0.1090909 0.0003964313 0.6979238 0.3151148897 0.8909690
0.1090909 0.0004342821 0.6980293 0.3149583333 0.8912395
0.1090909 0.0004757468 0.6981432 0.3146455270 0.8913072
0.1090909 0.0005211705 0.6982735 0.3148020833 0.8915777
0.1090909 0.0005709312 0.6983934 0.3147236520 0.8917807
0.1090909 0.0006254430 0.6985149 0.3145677083 0.8918483
0.1090909 0.0006851595 0.6986474 0.3144108456 0.8921526
0.1090909 0.0007505777 0.6987894 0.3140196078 0.8923218
0.1090909 0.0008222419 0.6989407 0.3134715074 0.8923894
0.1090909 0.0009007486 0.6990743 0.3129249387 0.8926263
0.1090909 0.0009867509 0.6992316 0.3130036765 0.8927955
0.1090909 0.0010809647 0.6993935 0.3125343137 0.8930998
0.1090909 0.0011841739 0.6995467 0.3116734069 0.8933365
0.1090909 0.0012972373 0.6997323 0.3112037377 0.8935055
0.1090909 0.0014210959 0.6999174 0.3107337623 0.8937422
0.1090909 0.0015567804 0.7000810 0.3108118873 0.8939789
0.1090909 0.0017054198 0.7002717 0.3103422181 0.8942157
0.1090909 0.0018682512 0.7004739 0.3093244485 0.8945538
0.1090909 0.0020466295 0.7006742 0.3086207108 0.8948242
0.1090909 0.0022420392 0.7008437 0.3084638480 0.8950272
0.1090909 0.0024561063 0.7010347 0.3075254289 0.8955682
0.1090909 0.0026906122 0.7012488 0.3066636029 0.8959403
0.1090909 0.0029475086 0.7014673 0.3057245711 0.8965489
0.1090909 0.0032289330 0.7016780 0.3053324142 0.8969545
0.1090909 0.0035372275 0.7019115 0.3042362132 0.8973941
0.1090909 0.0038749576 0.7021509 0.3028290441 0.8979692
0.1090909 0.0042449337 0.7023942 0.3012628676 0.8987135
0.1090909 0.0046502347 0.7026549 0.2989142157 0.8996265
0.1090909 0.0050942334 0.7028857 0.2973468137 0.9009457
0.1090909 0.0055806244 0.7031602 0.2957818627 0.9013178
0.1090909 0.0061134555 0.7033569 0.2936688113 0.9024004
0.1090909 0.0066971607 0.7035883 0.2915566789 0.9035165
0.1090909 0.0073365972 0.7038207 0.2890520833 0.9045650
0.1090909 0.0080370864 0.7040332 0.2867827819 0.9055458
0.1090909 0.0088044575 0.7042672 0.2844341299 0.9065939
0.1090909 0.0096450961 0.7044845 0.2819295343 0.9081834
0.1090909 0.0105659979 0.7047244 0.2787208946 0.9096716
0.1090909 0.0115748263 0.7049257 0.2766865809 0.9112612
0.1090909 0.0126799763 0.7051510 0.2736351103 0.9125799
0.1090909 0.0138906447 0.7053044 0.2708174020 0.9149132
0.1090909 0.0152169061 0.7054802 0.2669050245 0.9163336
0.1090909 0.0166697974 0.7056034 0.2630710784 0.9180242
0.1090909 0.0182614090 0.7056894 0.2593943015 0.9195460
0.1090909 0.0200049856 0.7057390 0.2549332108 0.9216767
0.1090909 0.0219150368 0.7058164 0.2521942402 0.9237733
0.1090909 0.0240074572 0.7058255 0.2482031250 0.9260055
0.1090909 0.0262996594 0.7058195 0.2432714461 0.9291166
0.1090909 0.0288107182 0.7057402 0.2376363358 0.9315177
0.1090909 0.0315615298 0.7055813 0.2313780637 0.9342570
0.1090909 0.0345749854 0.7054418 0.2247264093 0.9367261
0.1090909 0.0378761620 0.7052004 0.2176822917 0.9394317
0.1090909 0.0414925309 0.7048825 0.2095447304 0.9426781
0.1090909 0.0454541862 0.7045089 0.2004666054 0.9462287
0.1090909 0.0497940954 0.7040771 0.1932686887 0.9496106
0.1090909 0.0545483737 0.7034338 0.1844283088 0.9529928
0.1090909 0.0597565847 0.7025944 0.1764463848 0.9565435
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1.0000000 0.0012972373 0.7039541 0.2942953431 0.9040916
1.0000000 0.0014210959 0.7042028 0.2913219975 0.9050385
1.0000000 0.0015567804 0.7045249 0.2885024510 0.9060867
1.0000000 0.0017054198 0.7047752 0.2874053309 0.9074059
1.0000000 0.0018682512 0.7049837 0.2845101103 0.9085897
1.0000000 0.0020466295 0.7051509 0.2821605392 0.9101794
1.0000000 0.0022420392 0.7052863 0.2784068627 0.9121412
1.0000000 0.0024561063 0.7054520 0.2747291667 0.9141024
1.0000000 0.0026906122 0.7055963 0.2720698529 0.9157592
1.0000000 0.0029475086 0.7057221 0.2680805760 0.9173487
1.0000000 0.0032289330 0.7057990 0.2640903799 0.9193100
1.0000000 0.0035372275 0.7058497 0.2604096201 0.9217108
1.0000000 0.0038749576 0.7058327 0.2568890931 0.9233003
1.0000000 0.0042449337 0.7058204 0.2525079657 0.9263439
1.0000000 0.0046502347 0.7057912 0.2471072304 0.9293877
1.0000000 0.0050942334 0.7056216 0.2406856618 0.9316532
1.0000000 0.0055806244 0.7053734 0.2340315564 0.9345617
1.0000000 0.0061134555 0.7049607 0.2271446078 0.9376391
1.0000000 0.0066971607 0.7042941 0.2187760417 0.9408186
1.0000000 0.0073365972 0.7033092 0.2095435049 0.9445729
1.0000000 0.0080370864 0.7021229 0.2004672181 0.9478194
1.0000000 0.0088044575 0.7005941 0.1912349877 0.9509981
1.0000000 0.0096450961 0.6987826 0.1807472426 0.9550901
1.0000000 0.0105659979 0.6965957 0.1722172181 0.9593177
1.0000000 0.0115748263 0.6941787 0.1614203431 0.9624966
1.0000000 0.0126799763 0.6914722 0.1517172181 0.9655742
1.0000000 0.0138906447 0.6888280 0.1407653186 0.9681442
1.0000000 0.0152169061 0.6862463 0.1305153186 0.9713229
1.0000000 0.0166697974 0.6834533 0.1191669730 0.9739606
1.0000000 0.0182614090 0.6808647 0.1060992647 0.9763616
1.0000000 0.0200049856 0.6781403 0.0936599265 0.9793713
1.0000000 0.0219150368 0.6750640 0.0813731618 0.9828881
1.0000000 0.0240074572 0.6714517 0.0694004289 0.9859657
1.0000000 0.0262996594 0.6674120 0.0582913603 0.9886711
1.0000000 0.0288107182 0.6631770 0.0468667279 0.9911738
1.0000000 0.0315615298 0.6587900 0.0364601716 0.9928647
1.0000000 0.0345749854 0.6541755 0.0254255515 0.9947585
1.0000000 0.0378761620 0.6489235 0.0160370711 0.9964494
1.0000000 0.0414925309 0.6428743 0.0100140931 0.9980050
1.0000000 0.0454541862 0.6344934 0.0063379289 0.9985800
1.0000000 0.0497940954 0.6218078 0.0041476716 0.9988844
1.0000000 0.0545483737 0.6085731 0.0027380515 0.9992901
1.0000000 0.0597565847 0.6023817 0.0017212010 0.9995267
1.0000000 0.0654620691 0.6010482 0.0008602941 0.9997634
1.0000000 0.0717123061 0.6006989 0.0004690564 0.9999662
1.0000000 0.0785593079 0.5754109 0.0000000000 1.0000000
1.0000000 0.0860600530 0.5172964 0.0000000000 1.0000000
1.0000000 0.0942769599 0.5000000 0.0000000000 1.0000000
1.0000000 0.1032784069 0.5000000 0.0000000000 1.0000000
1.0000000 0.1131393008 0.5000000 0.0000000000 1.0000000
1.0000000 0.1239417006 0.5000000 0.0000000000 1.0000000
1.0000000 0.1357755002 0.5000000 0.0000000000 1.0000000
1.0000000 0.1487391762 0.5000000 0.0000000000 1.0000000
ROC was used to select the optimal model using the largest value.
The final values used for the model were alpha = 0.2909091 and lambda
= 0.01157483.
Visualization of Results
Let us apply the resamples ()train ()Chapter 5 ). Since we performed 10 repetitions of 5-fold cross validation, the metrics are computed on 10*5=50 validation samples.
<- list (rf = grid_search_rf,xgb = grid_search_xgb,svm = svm_model,glmnet = glmnet_model<- resamples (list_models)
We can have a look at a summary of the results. The summary function we used was twoClassSummary ()
Call:
summary.resamples(object = results)
Models: rf, xgb, svm, glmnet
Number of resamples: 50
ROC
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
rf 0.6379019 0.6728604 0.6813029 0.6832056 0.6911433 0.7353572 0
xgb 0.6347458 0.6648094 0.6822767 0.6792180 0.6959758 0.7092785 0
svm 0.6479087 0.6866009 0.6985173 0.6976585 0.7091895 0.7262056 0
glmnet 0.6574529 0.6932502 0.7074673 0.7060322 0.7193341 0.7372350 0
Sens
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
rf 0.09019608 0.1212125 0.1328125 0.1313686 0.1406250 0.1718750 0
xgb 0.52343750 0.5921569 0.6093750 0.6100214 0.6279297 0.6666667 0
svm 0.18359375 0.2226562 0.2348346 0.2356869 0.2539062 0.2823529 0
glmnet 0.19215686 0.2431641 0.2539062 0.2521146 0.2656250 0.2980392 0
Spec
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
rf 0.9373942 0.9632451 0.9670337 0.9677365 0.9746193 0.9847716 0
xgb 0.6074450 0.6380548 0.6534233 0.6520085 0.6653991 0.7010135 0
svm 0.8798646 0.9142736 0.9264588 0.9250566 0.9386897 0.9662162 0
glmnet 0.8883249 0.9170897 0.9256128 0.9246191 0.9324039 0.9459459 0
For each of the models, we can look at the performance metrics computed on the validation samples for the best set of hyperparameters.
Let us load the train and test samples (defined in Chapter 5 ):
load ("../data/out/df_train.rda" )load ("../data/out/df_test.rda" )
<- which (colnames (df_train) %in% c ("status" , "id" , "PERSONNE_statut" )
For each model, let us compute some additional metrics on the train and on the test set. We need to load the packages required to make predictions using the fitted models.
Warning: package 'randomForest' was built under R version 4.4.1
Type rfNews() to see new features/changes/bug fixes.
Attaching package: 'randomForest'
The following object is masked from 'package:dplyr':
combine
The following object is masked from 'package:ggplot2':
margin
Attaching package: 'ranger'
The following object is masked from 'package:randomForest':
importance
Attaching package: 'xgboost'
The following object is masked from 'package:dplyr':
slice
library (e1071)library (glmnet)
Loading required package: Matrix
Attaching package: 'Matrix'
The following objects are masked from 'package:tidyr':
expand, pack, unpack
Then, we can loop over the models and compute the predictions on both the train set and the test set. For each set, we focus on the accuracy, the sensitivity, and on the specificity.
<- NULL <- NULL <- NULL for (mod in list_models) {# Predictions <- predict (mod, newdata = df_train[,- ind_remove])<- predict (mod, newdata = df_test[- ind_remove])# Predicted scores <- predict (newdata = df_train[,- ind_remove], type = "prob" <- predict (newdata = df_test[,- ind_remove], type = "prob" # Confusion matrices <- confusionMatrix (pred_train, df_train$ status)<- confusionMatrix (pred_test, df_test$ status)# ROC Curves <- MLeval:: evalm (data.frame (pred_train_scores, df_train$ status), showplots = FALSE <- which (row.names (roc_train$ stdres$ Group1) == "AUC-ROC" )<- roc_train$ stdres$ Group1$ Score[ind_roc_train]<- MLeval:: evalm (data.frame (pred_test_scores, df_test$ status), showplots = FALSE <- which (row.names (roc_test$ stdres$ Group1) == "AUC-ROC" )<- roc_test$ stdres$ Group1$ Score[ind_roc_test]# Binding the results <- bind_rows (tibble (method = mod$ method, accuracy = cm_train$ overall["Accuracy" ],sensitivity = cm_train$ byClass[c ("Sensitivity" )],specificity = cm_train$ byClass[c ("Specificity" )],auc = roc_val_train<- bind_rows (tibble (method = mod$ method,accuracy = cm_test$ overall["Accuracy" ],sensitivity = cm_test$ byClass[c ("Sensitivity" )],specificity = cm_test$ byClass[c ("Specificity" )],auc = roc_val_test# And for the roc curves <- bind_rows ($ roc$ data |> mutate (model = mod$ method, sample = "train" ) |> bind_rows ($ roc$ data |> mutate (model = mod$ method, sample = "test" )
The performances metrics are shown in Table 18.1 .
Code
library (kableExtra)|> mutate (sample = "Train" ) |> bind_rows (|> mutate (sample = "Test" )|> mutate (sample = factor (sample, levels = c ("Train" , "Test" ))) |> pivot_wider (names_from = "sample" , values_from = c ("accuracy" , "sensitivity" , "specificity" , "auc" )|> kable (align= c ('l' , rep ('c' , 7 )), digits = 3 , col.names = c ("Method" , rep (c ("Train" , "Test" ), 4 ))|> kable_styling ("striped" ) %>% add_header_above (c (" " = 1 , "Accuracy" = 2 , "Sensitivity" = 2 , "Specificity" = 2 , "AUC" = 2 )
The ROC curves:
Code
source ("../Codes/utils.R" )<- ggplot (data = roc_curves |> mutate (model = factor (levels = c ("ranger" , "xgbTree" , "svmRadial" , "glmnet" ),labels = c ("Random Forest" , "XGBoost" , "SVM" , "Penalized Logistic Regression" sample = factor (levels = c ("train" , "test" ), labels = c ("Train" , "Test" )mapping = aes (x = FPR, y = SENS, colour = model, linetype = model)) + geom_line () + scale_colour_manual (NULL , values = c ("Random Forest" = "#009E73" , "XGBoost" = "#D55E00" , "SVM" = "#56B4E9" , "Penalized Logistic Regression" = "#CC79A7" + scale_linetype_discrete (NULL ) + theme_paper () + labs (x = "False positive rate" , y = "True positive rate" ) + facet_wrap (~ sample)
