This chapter presents the results obtained with the classifier trained in Chapter 17 .
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✔ 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 17 .
load ("../data/out/estim/v3/grid_search_rf_sah.rda" )load ("../data/out/estim/v3/grid_search_xgb_sah.rda" )load ("../data/out/estim/v3/svm_model_sah.rda" )load ("../data/out/estim/v3/glmnet_model_sah.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
4305 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: 3445, 3444, 3444, 3444, 3443, 3444, ...
Resampling results across tuning parameters:
mtry min.node.size ROC Sens Spec
3 50 0.6730495 0.0092635479 0.9983834
3 75 0.6736373 0.0048655242 0.9993072
3 100 0.6731015 0.0020404508 0.9998680
3 150 0.6726082 0.0001568627 0.9999340
4 50 0.6739269 0.0212743554 0.9966017
4 75 0.6742251 0.0120901652 0.9981853
4 100 0.6736399 0.0071428130 0.9987793
4 150 0.6732947 0.0024322989 0.9998350
5 50 0.6748114 0.0306157172 0.9941277
5 75 0.6743623 0.0199404045 0.9965360
5 100 0.6736946 0.0135022387 0.9980535
5 150 0.6732621 0.0053382739 0.9992411
6 50 0.6746108 0.0409013432 0.9910923
6 75 0.6741555 0.0284180948 0.9944907
6 100 0.6734979 0.0204894241 0.9967337
6 150 0.6737092 0.0089482785 0.9987463
7 50 0.6736712 0.0492209356 0.9893106
7 75 0.6743368 0.0350125058 0.9928407
7 100 0.6742522 0.0265354331 0.9948864
7 150 0.6731619 0.0132657094 0.9980204
8 50 0.6736567 0.0580114250 0.9865721
8 75 0.6739156 0.0427818434 0.9907951
8 100 0.6735730 0.0328124132 0.9936326
8 150 0.6739220 0.0178974834 0.9969319
9 50 0.6742330 0.0629559981 0.9851537
9 75 0.6738231 0.0487494210 0.9895413
9 100 0.6738584 0.0376038289 0.9919172
9 150 0.6732811 0.0216668211 0.9957438
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
4305 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: 3444, 3444, 3444, 3445, 3443, 3444, ...
Resampling results across tuning parameters:
max_depth gamma colsample_bytree min_child_weight subsample ROC
3 0 0.1 50 0.7 0.6785363
3 0 0.1 50 0.8 0.6799875
3 0 0.1 50 0.9 0.6804500
3 0 0.1 50 1.0 0.6823648
3 0 0.1 100 0.7 0.6740347
3 0 0.1 100 0.8 0.6750796
3 0 0.1 100 0.9 0.6767909
3 0 0.1 100 1.0 0.6769700
3 0 0.1 150 0.7 0.6574394
3 0 0.1 150 0.8 0.6664940
3 0 0.1 150 0.9 0.6716528
3 0 0.1 150 1.0 0.6742771
3 0 0.2 50 0.7 0.6823229
3 0 0.2 50 0.8 0.6826816
3 0 0.2 50 0.9 0.6830717
3 0 0.2 50 1.0 0.6841141
3 0 0.2 100 0.7 0.6787506
3 0 0.2 100 0.8 0.6799038
3 0 0.2 100 0.9 0.6796900
3 0 0.2 100 1.0 0.6796796
3 0 0.2 150 0.7 0.6628077
3 0 0.2 150 0.8 0.6714751
3 0 0.2 150 0.9 0.6759224
3 0 0.2 150 1.0 0.6772249
3 0 0.3 50 0.7 0.6829134
3 0 0.3 50 0.8 0.6832605
3 0 0.3 50 0.9 0.6838021
3 0 0.3 50 1.0 0.6850128
3 0 0.3 100 0.7 0.6799040
3 0 0.3 100 0.8 0.6805631
3 0 0.3 100 0.9 0.6807553
3 0 0.3 100 1.0 0.6812284
3 0 0.3 150 0.7 0.6653150
3 0 0.3 150 0.8 0.6731625
3 0 0.3 150 0.9 0.6769188
3 0 0.3 150 1.0 0.6789956
3 0 0.4 50 0.7 0.6835646
3 0 0.4 50 0.8 0.6843785
3 0 0.4 50 0.9 0.6837642
3 0 0.4 50 1.0 0.6849679
3 0 0.4 100 0.7 0.6805164
3 0 0.4 100 0.8 0.6807898
3 0 0.4 100 0.9 0.6809159
3 0 0.4 100 1.0 0.6812688
3 0 0.4 150 0.7 0.6660034
3 0 0.4 150 0.8 0.6739234
3 0 0.4 150 0.9 0.6775012
3 0 0.4 150 1.0 0.6793173
3 0 0.5 50 0.7 0.6838229
3 0 0.5 50 0.8 0.6840511
3 0 0.5 50 0.9 0.6837561
3 0 0.5 50 1.0 0.6845494
3 0 0.5 100 0.7 0.6808015
3 0 0.5 100 0.8 0.6814586
3 0 0.5 100 0.9 0.6813246
3 0 0.5 100 1.0 0.6810916
3 0 0.5 150 0.7 0.6666182
3 0 0.5 150 0.8 0.6745138
3 0 0.5 150 0.9 0.6779811
3 0 0.5 150 1.0 0.6793098
3 0 0.6 50 0.7 0.6835108
3 0 0.6 50 0.8 0.6840414
3 0 0.6 50 0.9 0.6838978
3 0 0.6 50 1.0 0.6842257
3 0 0.6 100 0.7 0.6808373
3 0 0.6 100 0.8 0.6812973
3 0 0.6 100 0.9 0.6807972
3 0 0.6 100 1.0 0.6809873
3 0 0.6 150 0.7 0.6670491
3 0 0.6 150 0.8 0.6745420
3 0 0.6 150 0.9 0.6778977
3 0 0.6 150 1.0 0.6794917
3 0 0.7 50 0.7 0.6836444
3 0 0.7 50 0.8 0.6839783
3 0 0.7 50 0.9 0.6838917
3 0 0.7 50 1.0 0.6841779
3 0 0.7 100 0.7 0.6810108
3 0 0.7 100 0.8 0.6817369
3 0 0.7 100 0.9 0.6811319
3 0 0.7 100 1.0 0.6806386
3 0 0.7 150 0.7 0.6672749
3 0 0.7 150 0.8 0.6745525
3 0 0.7 150 0.9 0.6778563
3 0 0.7 150 1.0 0.6790577
3 0 0.8 50 0.7 0.6837489
3 0 0.8 50 0.8 0.6843278
3 0 0.8 50 0.9 0.6839312
3 0 0.8 50 1.0 0.6840723
3 0 0.8 100 0.7 0.6812223
3 0 0.8 100 0.8 0.6817090
3 0 0.8 100 0.9 0.6813720
3 0 0.8 100 1.0 0.6804816
3 0 0.8 150 0.7 0.6673856
3 0 0.8 150 0.8 0.6749276
3 0 0.8 150 0.9 0.6783571
3 0 0.8 150 1.0 0.6789135
3 0 0.9 50 0.7 0.6833488
3 0 0.9 50 0.8 0.6840435
3 0 0.9 50 0.9 0.6838433
3 0 0.9 50 1.0 0.6838195
3 0 0.9 100 0.7 0.6812963
3 0 0.9 100 0.8 0.6819089
3 0 0.9 100 0.9 0.6811500
3 0 0.9 100 1.0 0.6803536
3 0 0.9 150 0.7 0.6674431
3 0 0.9 150 0.8 0.6748335
3 0 0.9 150 0.9 0.6785783
3 0 0.9 150 1.0 0.6784079
3 0 1.0 50 0.7 0.6836496
3 0 1.0 50 0.8 0.6844347
3 0 1.0 50 0.9 0.6837424
3 0 1.0 50 1.0 0.6833267
3 0 1.0 100 0.7 0.6815057
3 0 1.0 100 0.8 0.6815743
3 0 1.0 100 0.9 0.6813530
3 0 1.0 100 1.0 0.6802621
3 0 1.0 150 0.7 0.6674454
3 0 1.0 150 0.8 0.6751188
3 0 1.0 150 0.9 0.6783930
3 0 1.0 150 1.0 0.6782824
3 5 0.1 50 0.7 0.6784367
3 5 0.1 50 0.8 0.6801792
3 5 0.1 50 0.9 0.6809131
3 5 0.1 50 1.0 0.6827148
3 5 0.1 100 0.7 0.6728828
3 5 0.1 100 0.8 0.6744917
3 5 0.1 100 0.9 0.6762808
3 5 0.1 100 1.0 0.6763473
3 5 0.1 150 0.7 0.6570285
3 5 0.1 150 0.8 0.6656909
3 5 0.1 150 0.9 0.6705388
3 5 0.1 150 1.0 0.6735731
3 5 0.2 50 0.7 0.6823980
3 5 0.2 50 0.8 0.6828969
3 5 0.2 50 0.9 0.6828840
3 5 0.2 50 1.0 0.6843282
3 5 0.2 100 0.7 0.6785868
3 5 0.2 100 0.8 0.6791173
3 5 0.2 100 0.9 0.6796671
3 5 0.2 100 1.0 0.6795147
3 5 0.2 150 0.7 0.6626534
3 5 0.2 150 0.8 0.6712973
3 5 0.2 150 0.9 0.6754683
3 5 0.2 150 1.0 0.6769809
3 5 0.3 50 0.7 0.6831722
3 5 0.3 50 0.8 0.6841054
3 5 0.3 50 0.9 0.6842646
3 5 0.3 50 1.0 0.6848902
3 5 0.3 100 0.7 0.6794977
3 5 0.3 100 0.8 0.6806805
3 5 0.3 100 0.9 0.6807250
3 5 0.3 100 1.0 0.6811460
3 5 0.3 150 0.7 0.6649378
3 5 0.3 150 0.8 0.6730824
3 5 0.3 150 0.9 0.6766321
3 5 0.3 150 1.0 0.6783281
3 5 0.4 50 0.7 0.6835420
3 5 0.4 50 0.8 0.6841345
3 5 0.4 50 0.9 0.6839566
3 5 0.4 50 1.0 0.6846661
3 5 0.4 100 0.7 0.6806217
3 5 0.4 100 0.8 0.6811754
3 5 0.4 100 0.9 0.6809684
3 5 0.4 100 1.0 0.6811957
3 5 0.4 150 0.7 0.6657427
3 5 0.4 150 0.8 0.6739782
3 5 0.4 150 0.9 0.6774749
3 5 0.4 150 1.0 0.6794038
3 5 0.5 50 0.7 0.6834947
3 5 0.5 50 0.8 0.6842232
3 5 0.5 50 0.9 0.6838899
3 5 0.5 50 1.0 0.6844803
3 5 0.5 100 0.7 0.6808606
3 5 0.5 100 0.8 0.6809895
3 5 0.5 100 0.9 0.6811073
3 5 0.5 100 1.0 0.6812405
3 5 0.5 150 0.7 0.6664351
3 5 0.5 150 0.8 0.6743021
3 5 0.5 150 0.9 0.6782797
3 5 0.5 150 1.0 0.6793346
3 5 0.6 50 0.7 0.6839208
3 5 0.6 50 0.8 0.6844647
3 5 0.6 50 0.9 0.6842903
3 5 0.6 50 1.0 0.6845620
3 5 0.6 100 0.7 0.6810347
3 5 0.6 100 0.8 0.6812369
3 5 0.6 100 0.9 0.6814214
3 5 0.6 100 1.0 0.6809212
3 5 0.6 150 0.7 0.6666044
3 5 0.6 150 0.8 0.6743755
3 5 0.6 150 0.9 0.6782092
3 5 0.6 150 1.0 0.6793694
3 5 0.7 50 0.7 0.6836577
3 5 0.7 50 0.8 0.6845236
3 5 0.7 50 0.9 0.6841699
3 5 0.7 50 1.0 0.6841241
3 5 0.7 100 0.7 0.6806693
3 5 0.7 100 0.8 0.6816725
3 5 0.7 100 0.9 0.6812272
3 5 0.7 100 1.0 0.6807908
3 5 0.7 150 0.7 0.6669845
3 5 0.7 150 0.8 0.6745803
3 5 0.7 150 0.9 0.6781941
3 5 0.7 150 1.0 0.6790916
3 5 0.8 50 0.7 0.6839334
3 5 0.8 50 0.8 0.6843188
3 5 0.8 50 0.9 0.6839173
3 5 0.8 50 1.0 0.6837272
3 5 0.8 100 0.7 0.6814689
3 5 0.8 100 0.8 0.6816174
3 5 0.8 100 0.9 0.6817295
3 5 0.8 100 1.0 0.6807373
3 5 0.8 150 0.7 0.6673576
3 5 0.8 150 0.8 0.6747715
3 5 0.8 150 0.9 0.6781211
3 5 0.8 150 1.0 0.6788837
3 5 0.9 50 0.7 0.6834884
3 5 0.9 50 0.8 0.6842008
3 5 0.9 50 0.9 0.6839661
3 5 0.9 50 1.0 0.6838640
3 5 0.9 100 0.7 0.6808610
3 5 0.9 100 0.8 0.6814620
3 5 0.9 100 0.9 0.6815448
3 5 0.9 100 1.0 0.6805508
3 5 0.9 150 0.7 0.6675919
3 5 0.9 150 0.8 0.6745821
3 5 0.9 150 0.9 0.6783292
3 5 0.9 150 1.0 0.6786147
3 5 1.0 50 0.7 0.6836123
3 5 1.0 50 0.8 0.6845634
3 5 1.0 50 0.9 0.6837803
3 5 1.0 50 1.0 0.6833642
3 5 1.0 100 0.7 0.6816649
3 5 1.0 100 0.8 0.6816864
3 5 1.0 100 0.9 0.6816138
3 5 1.0 100 1.0 0.6804587
3 5 1.0 150 0.7 0.6672914
3 5 1.0 150 0.8 0.6752591
3 5 1.0 150 0.9 0.6782267
3 5 1.0 150 1.0 0.6783631
3 10 0.1 50 0.7 0.6769570
3 10 0.1 50 0.8 0.6780180
3 10 0.1 50 0.9 0.6796155
3 10 0.1 50 1.0 0.6807513
3 10 0.1 100 0.7 0.6700527
3 10 0.1 100 0.8 0.6719129
3 10 0.1 100 0.9 0.6742686
3 10 0.1 100 1.0 0.6748560
3 10 0.1 150 0.7 0.6527969
3 10 0.1 150 0.8 0.6625560
3 10 0.1 150 0.9 0.6679017
3 10 0.1 150 1.0 0.6713966
3 10 0.2 50 0.7 0.6809766
3 10 0.2 50 0.8 0.6821112
3 10 0.2 50 0.9 0.6823345
3 10 0.2 50 1.0 0.6837345
3 10 0.2 100 0.7 0.6755791
3 10 0.2 100 0.8 0.6775316
3 10 0.2 100 0.9 0.6776636
3 10 0.2 100 1.0 0.6776296
3 10 0.2 150 0.7 0.6596128
3 10 0.2 150 0.8 0.6686204
3 10 0.2 150 0.9 0.6727705
3 10 0.2 150 1.0 0.6748224
3 10 0.3 50 0.7 0.6822670
3 10 0.3 50 0.8 0.6831851
3 10 0.3 50 0.9 0.6831822
3 10 0.3 50 1.0 0.6843161
3 10 0.3 100 0.7 0.6775455
3 10 0.3 100 0.8 0.6786321
3 10 0.3 100 0.9 0.6791556
3 10 0.3 100 1.0 0.6789010
3 10 0.3 150 0.7 0.6621308
3 10 0.3 150 0.8 0.6706682
3 10 0.3 150 0.9 0.6745561
3 10 0.3 150 1.0 0.6758722
3 10 0.4 50 0.7 0.6833161
3 10 0.4 50 0.8 0.6836598
3 10 0.4 50 0.9 0.6836312
3 10 0.4 50 1.0 0.6845025
3 10 0.4 100 0.7 0.6789957
3 10 0.4 100 0.8 0.6795424
3 10 0.4 100 0.9 0.6798404
3 10 0.4 100 1.0 0.6790884
3 10 0.4 150 0.7 0.6635092
3 10 0.4 150 0.8 0.6716255
3 10 0.4 150 0.9 0.6755761
3 10 0.4 150 1.0 0.6766906
3 10 0.5 50 0.7 0.6834997
3 10 0.5 50 0.8 0.6838629
3 10 0.5 50 0.9 0.6835847
3 10 0.5 50 1.0 0.6841762
3 10 0.5 100 0.7 0.6793281
3 10 0.5 100 0.8 0.6798023
3 10 0.5 100 0.9 0.6803992
3 10 0.5 100 1.0 0.6793602
3 10 0.5 150 0.7 0.6642946
3 10 0.5 150 0.8 0.6725202
3 10 0.5 150 0.9 0.6762016
3 10 0.5 150 1.0 0.6770255
3 10 0.6 50 0.7 0.6836984
3 10 0.6 50 0.8 0.6844185
3 10 0.6 50 0.9 0.6838703
3 10 0.6 50 1.0 0.6839984
3 10 0.6 100 0.7 0.6796540
3 10 0.6 100 0.8 0.6801761
3 10 0.6 100 0.9 0.6806752
3 10 0.6 100 1.0 0.6791478
3 10 0.6 150 0.7 0.6649074
3 10 0.6 150 0.8 0.6730252
3 10 0.6 150 0.9 0.6767059
3 10 0.6 150 1.0 0.6769414
3 10 0.7 50 0.7 0.6837089
3 10 0.7 50 0.8 0.6845445
3 10 0.7 50 0.9 0.6840866
3 10 0.7 50 1.0 0.6835749
3 10 0.7 100 0.7 0.6801369
3 10 0.7 100 0.8 0.6807163
3 10 0.7 100 0.9 0.6806090
3 10 0.7 100 1.0 0.6782027
3 10 0.7 150 0.7 0.6653787
3 10 0.7 150 0.8 0.6733405
3 10 0.7 150 0.9 0.6769530
3 10 0.7 150 1.0 0.6768070
3 10 0.8 50 0.7 0.6839297
3 10 0.8 50 0.8 0.6845420
3 10 0.8 50 0.9 0.6840121
3 10 0.8 50 1.0 0.6833153
3 10 0.8 100 0.7 0.6802750
3 10 0.8 100 0.8 0.6808074
3 10 0.8 100 0.9 0.6808027
3 10 0.8 100 1.0 0.6779320
3 10 0.8 150 0.7 0.6658129
3 10 0.8 150 0.8 0.6733939
3 10 0.8 150 0.9 0.6773002
3 10 0.8 150 1.0 0.6759357
3 10 0.9 50 0.7 0.6842033
3 10 0.9 50 0.8 0.6844227
3 10 0.9 50 0.9 0.6846245
3 10 0.9 50 1.0 0.6829669
3 10 0.9 100 0.7 0.6804863
3 10 0.9 100 0.8 0.6805764
3 10 0.9 100 0.9 0.6808074
3 10 0.9 100 1.0 0.6774826
3 10 0.9 150 0.7 0.6660555
3 10 0.9 150 0.8 0.6737338
3 10 0.9 150 0.9 0.6774274
3 10 0.9 150 1.0 0.6750149
3 10 1.0 50 0.7 0.6842948
3 10 1.0 50 0.8 0.6844909
3 10 1.0 50 0.9 0.6839677
3 10 1.0 50 1.0 0.6817418
3 10 1.0 100 0.7 0.6809214
3 10 1.0 100 0.8 0.6810347
3 10 1.0 100 0.9 0.6812914
3 10 1.0 100 1.0 0.6747461
3 10 1.0 150 0.7 0.6665210
3 10 1.0 150 0.8 0.6738406
3 10 1.0 150 0.9 0.6773406
3 10 1.0 150 1.0 0.6726771
4 0 0.1 50 0.7 0.6785887
4 0 0.1 50 0.8 0.6804697
4 0 0.1 50 0.9 0.6808061
4 0 0.1 50 1.0 0.6827800
4 0 0.1 100 0.7 0.6747017
4 0 0.1 100 0.8 0.6759857
4 0 0.1 100 0.9 0.6777560
4 0 0.1 100 1.0 0.6777733
4 0 0.1 150 0.7 0.6569400
4 0 0.1 150 0.8 0.6666460
4 0 0.1 150 0.9 0.6717537
4 0 0.1 150 1.0 0.6749157
4 0 0.2 50 0.7 0.6833539
4 0 0.2 50 0.8 0.6842908
4 0 0.2 50 0.9 0.6846659
4 0 0.2 50 1.0 0.6856661
4 0 0.2 100 0.7 0.6798656
4 0 0.2 100 0.8 0.6810590
4 0 0.2 100 0.9 0.6819928
4 0 0.2 100 1.0 0.6820673
4 0 0.2 150 0.7 0.6632031
4 0 0.2 150 0.8 0.6718989
4 0 0.2 150 0.9 0.6769270
4 0 0.2 150 1.0 0.6786762
4 0 0.3 50 0.7 0.6839596
4 0 0.3 50 0.8 0.6848924
4 0 0.3 50 0.9 0.6852112
4 0 0.3 50 1.0 0.6862114
4 0 0.3 100 0.7 0.6816880
4 0 0.3 100 0.8 0.6828388
4 0 0.3 100 0.9 0.6829039
4 0 0.3 100 1.0 0.6833864
4 0 0.3 150 0.7 0.6651174
4 0 0.3 150 0.8 0.6740691
4 0 0.3 150 0.9 0.6786712
4 0 0.3 150 1.0 0.6805926
4 0 0.4 50 0.7 0.6846938
4 0 0.4 50 0.8 0.6848836
4 0 0.4 50 0.9 0.6855421
4 0 0.4 50 1.0 0.6864786
4 0 0.4 100 0.7 0.6823251
4 0 0.4 100 0.8 0.6830910
4 0 0.4 100 0.9 0.6829714
4 0 0.4 100 1.0 0.6835639
4 0 0.4 150 0.7 0.6661499
4 0 0.4 150 0.8 0.6745502
4 0 0.4 150 0.9 0.6794759
4 0 0.4 150 1.0 0.6815686
4 0 0.5 50 0.7 0.6837851
4 0 0.5 50 0.8 0.6845903
4 0 0.5 50 0.9 0.6849844
4 0 0.5 50 1.0 0.6864665
4 0 0.5 100 0.7 0.6823526
4 0 0.5 100 0.8 0.6834240
4 0 0.5 100 0.9 0.6835065
4 0 0.5 100 1.0 0.6837519
4 0 0.5 150 0.7 0.6662796
4 0 0.5 150 0.8 0.6753265
4 0 0.5 150 0.9 0.6796613
4 0 0.5 150 1.0 0.6815626
4 0 0.6 50 0.7 0.6838439
4 0 0.6 50 0.8 0.6846989
4 0 0.6 50 0.9 0.6847631
4 0 0.6 50 1.0 0.6861180
4 0 0.6 100 0.7 0.6825915
4 0 0.6 100 0.8 0.6832943
4 0 0.6 100 0.9 0.6834876
4 0 0.6 100 1.0 0.6832892
4 0 0.6 150 0.7 0.6667859
4 0 0.6 150 0.8 0.6751991
4 0 0.6 150 0.9 0.6799837
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5 5 0.9 50 1.0 0.6839312
5 5 0.9 100 0.7 0.6833563
5 5 0.9 100 0.8 0.6837091
5 5 0.9 100 0.9 0.6836783
5 5 0.9 100 1.0 0.6829414
5 5 0.9 150 0.7 0.6673175
5 5 0.9 150 0.8 0.6755940
5 5 0.9 150 0.9 0.6803319
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5 5 1.0 50 0.7 0.6835151
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5 5 1.0 100 0.7 0.6832146
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5 5 1.0 100 0.9 0.6833686
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5 5 1.0 150 0.7 0.6678493
5 5 1.0 150 0.8 0.6758227
5 5 1.0 150 0.9 0.6801083
5 5 1.0 150 1.0 0.6816326
5 10 0.1 50 0.7 0.6787672
5 10 0.1 50 0.8 0.6807710
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5 10 0.1 100 0.7 0.6711287
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5 10 0.1 100 0.9 0.6751065
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5 10 0.1 150 0.7 0.6526216
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5 10 0.3 50 0.9 0.6853068
5 10 0.3 50 1.0 0.6870771
5 10 0.3 100 0.7 0.6789161
5 10 0.3 100 0.8 0.6806054
5 10 0.3 100 0.9 0.6816322
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5 10 0.3 150 0.7 0.6624065
5 10 0.3 150 0.8 0.6712269
5 10 0.3 150 0.9 0.6759670
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5 10 0.4 50 0.7 0.6854682
5 10 0.4 50 0.8 0.6858807
5 10 0.4 50 0.9 0.6857422
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5 10 0.5 50 0.7 0.6852249
5 10 0.5 50 0.8 0.6858524
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5 10 0.5 100 0.7 0.6810531
5 10 0.5 100 0.8 0.6821874
5 10 0.5 100 0.9 0.6829608
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5 10 0.5 150 0.7 0.6645601
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5 10 0.6 50 0.7 0.6848495
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5 10 0.6 100 0.9 0.6832381
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5 10 0.6 150 0.9 0.6781436
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5 10 0.7 50 0.7 0.6852050
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5 10 0.7 50 0.9 0.6852972
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5 10 0.7 100 0.7 0.6817022
5 10 0.7 100 0.8 0.6825856
5 10 0.7 100 0.9 0.6832917
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5 10 0.7 150 0.8 0.6738279
5 10 0.7 150 0.9 0.6788473
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5 10 0.8 50 0.8 0.6851700
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5 10 0.8 150 0.7 0.6658496
5 10 0.8 150 0.8 0.6740921
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5 10 0.9 50 0.7 0.6847510
5 10 0.9 50 0.8 0.6851591
5 10 0.9 50 0.9 0.6847320
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5 10 0.9 100 0.7 0.6822552
5 10 0.9 100 0.8 0.6831392
5 10 0.9 100 0.9 0.6835251
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5 10 0.9 150 0.7 0.6660734
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5 10 0.9 150 0.9 0.6792564
5 10 0.9 150 1.0 0.6781306
5 10 1.0 50 0.7 0.6846618
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5 10 1.0 50 1.0 0.6812902
5 10 1.0 100 0.7 0.6819927
5 10 1.0 100 0.8 0.6829771
5 10 1.0 100 0.9 0.6831484
5 10 1.0 100 1.0 0.6770919
5 10 1.0 150 0.7 0.6664281
5 10 1.0 150 0.8 0.6745320
5 10 1.0 150 0.9 0.6794421
5 10 1.0 150 1.0 0.6751636
6 0 0.1 50 0.7 0.6789033
6 0 0.1 50 0.8 0.6796699
6 0 0.1 50 0.9 0.6803287
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6 0 0.1 100 0.8 0.6760193
6 0 0.1 100 0.9 0.6773513
6 0 0.1 100 1.0 0.6782374
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6 0 0.1 150 0.8 0.6664244
6 0 0.1 150 0.9 0.6715605
6 0 0.1 150 1.0 0.6748438
6 0 0.2 50 0.7 0.6828132
6 0 0.2 50 0.8 0.6837239
6 0 0.2 50 0.9 0.6842126
6 0 0.2 50 1.0 0.6852222
6 0 0.2 100 0.7 0.6796432
6 0 0.2 100 0.8 0.6813297
6 0 0.2 100 0.9 0.6820689
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6 0 0.2 150 0.7 0.6629298
6 0 0.2 150 0.8 0.6719308
6 0 0.2 150 0.9 0.6769361
6 0 0.2 150 1.0 0.6785543
6 0 0.3 50 0.7 0.6842676
6 0 0.3 50 0.8 0.6841044
6 0 0.3 50 0.9 0.6846884
6 0 0.3 50 1.0 0.6860523
6 0 0.3 100 0.7 0.6813592
6 0 0.3 100 0.8 0.6834398
6 0 0.3 100 0.9 0.6831011
6 0 0.3 100 1.0 0.6837251
6 0 0.3 150 0.7 0.6654438
6 0 0.3 150 0.8 0.6740689
6 0 0.3 150 0.9 0.6785410
6 0 0.3 150 1.0 0.6811871
6 0 0.4 50 0.7 0.6837592
6 0 0.4 50 0.8 0.6843490
6 0 0.4 50 0.9 0.6840818
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6 0 0.4 100 0.7 0.6815652
6 0 0.4 100 0.8 0.6832582
6 0 0.4 100 0.9 0.6838089
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6 0 0.5 50 0.9 0.6842596
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6 0 0.5 150 0.9 0.6798848
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6 0 0.6 50 0.7 0.6832425
6 0 0.6 50 0.8 0.6835912
6 0 0.6 50 0.9 0.6837306
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6 0 0.6 100 0.8 0.6836063
6 0 0.6 100 0.9 0.6843224
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6 0 0.6 150 0.9 0.6800994
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6 0 0.7 50 0.7 0.6834328
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6 0 0.7 150 0.8 0.6755959
6 0 0.7 150 0.9 0.6802408
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6 0 0.8 50 0.7 0.6827605
6 0 0.8 50 0.8 0.6832434
6 0 0.8 50 0.9 0.6832397
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6 0 0.8 100 0.8 0.6836589
6 0 0.8 100 0.9 0.6836115
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6 0 0.8 150 0.7 0.6675122
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6 0 0.8 150 0.9 0.6805686
6 0 0.8 150 1.0 0.6820855
6 0 0.9 50 0.7 0.6824600
6 0 0.9 50 0.8 0.6829487
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6 0 0.9 50 1.0 0.6826662
6 0 0.9 100 0.7 0.6829645
6 0 0.9 100 0.8 0.6838400
6 0 0.9 100 0.9 0.6836754
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6 0 0.9 150 0.7 0.6672100
6 0 0.9 150 0.8 0.6756394
6 0 0.9 150 0.9 0.6803485
6 0 0.9 150 1.0 0.6815666
6 0 1.0 50 0.7 0.6824051
6 0 1.0 50 0.8 0.6826627
6 0 1.0 50 0.9 0.6822590
6 0 1.0 50 1.0 0.6813155
6 0 1.0 100 0.7 0.6832275
6 0 1.0 100 0.8 0.6838054
6 0 1.0 100 0.9 0.6834674
6 0 1.0 100 1.0 0.6821873
6 0 1.0 150 0.7 0.6677452
6 0 1.0 150 0.8 0.6756071
6 0 1.0 150 0.9 0.6804864
6 0 1.0 150 1.0 0.6813780
6 5 0.1 50 0.7 0.6793149
6 5 0.1 50 0.8 0.6804424
6 5 0.1 50 0.9 0.6810147
6 5 0.1 50 1.0 0.6828420
6 5 0.1 100 0.7 0.6740513
6 5 0.1 100 0.8 0.6753460
6 5 0.1 100 0.9 0.6774441
6 5 0.1 100 1.0 0.6775696
6 5 0.1 150 0.7 0.6562940
6 5 0.1 150 0.8 0.6654116
6 5 0.1 150 0.9 0.6706818
6 5 0.1 150 1.0 0.6739963
6 5 0.2 50 0.7 0.6833538
6 5 0.2 50 0.8 0.6847979
6 5 0.2 50 0.9 0.6850152
6 5 0.2 50 1.0 0.6858724
6 5 0.2 100 0.7 0.6797034
6 5 0.2 100 0.8 0.6809551
6 5 0.2 100 0.9 0.6820694
6 5 0.2 100 1.0 0.6819835
6 5 0.2 150 0.7 0.6623492
6 5 0.2 150 0.8 0.6719448
6 5 0.2 150 0.9 0.6764423
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6 5 0.3 50 0.7 0.6845818
6 5 0.3 50 0.8 0.6851256
6 5 0.3 50 0.9 0.6851136
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6 5 0.3 100 0.7 0.6814975
6 5 0.3 100 0.8 0.6824744
6 5 0.3 100 0.9 0.6833290
6 5 0.3 100 1.0 0.6839111
6 5 0.3 150 0.7 0.6653404
6 5 0.3 150 0.8 0.6738497
6 5 0.3 150 0.9 0.6783411
6 5 0.3 150 1.0 0.6808218
6 5 0.4 50 0.7 0.6844102
6 5 0.4 50 0.8 0.6844982
6 5 0.4 50 0.9 0.6846379
6 5 0.4 50 1.0 0.6863241
6 5 0.4 100 0.7 0.6819102
6 5 0.4 100 0.8 0.6835368
6 5 0.4 100 0.9 0.6840066
6 5 0.4 100 1.0 0.6841620
6 5 0.4 150 0.7 0.6662174
6 5 0.4 150 0.8 0.6744437
6 5 0.4 150 0.9 0.6792730
6 5 0.4 150 1.0 0.6815074
6 5 0.5 50 0.7 0.6842007
6 5 0.5 50 0.8 0.6846457
6 5 0.5 50 0.9 0.6843817
6 5 0.5 50 1.0 0.6860173
6 5 0.5 100 0.7 0.6826506
6 5 0.5 100 0.8 0.6835720
6 5 0.5 100 0.9 0.6838714
6 5 0.5 100 1.0 0.6841346
6 5 0.5 150 0.7 0.6663788
6 5 0.5 150 0.8 0.6752978
6 5 0.5 150 0.9 0.6794139
6 5 0.5 150 1.0 0.6823026
6 5 0.6 50 0.7 0.6839560
6 5 0.6 50 0.8 0.6842523
6 5 0.6 50 0.9 0.6841821
6 5 0.6 50 1.0 0.6860117
6 5 0.6 100 0.7 0.6826930
6 5 0.6 100 0.8 0.6833857
6 5 0.6 100 0.9 0.6843785
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6 5 0.6 150 0.8 0.6756040
6 5 0.6 150 0.9 0.6800008
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6 5 0.7 50 0.7 0.6834186
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6 5 0.7 50 0.9 0.6838816
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6 5 0.7 100 0.9 0.6839853
6 5 0.7 100 1.0 0.6835323
6 5 0.7 150 0.7 0.6667593
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6 5 0.9 100 0.9 0.6839099
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6 5 0.9 150 0.8 0.6758567
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6 5 1.0 50 0.7 0.6827648
6 5 1.0 50 0.8 0.6826150
6 5 1.0 50 0.9 0.6825652
6 5 1.0 50 1.0 0.6820492
6 5 1.0 100 0.7 0.6828845
6 5 1.0 100 0.8 0.6838814
6 5 1.0 100 0.9 0.6835560
6 5 1.0 100 1.0 0.6823440
6 5 1.0 150 0.7 0.6677258
6 5 1.0 150 0.8 0.6753766
6 5 1.0 150 0.9 0.6805877
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6 10 0.1 50 0.7 0.6785781
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6 10 0.1 50 1.0 0.6836752
6 10 0.1 100 0.7 0.6711258
6 10 0.1 100 0.8 0.6729055
6 10 0.1 100 0.9 0.6751260
6 10 0.1 100 1.0 0.6762195
6 10 0.1 150 0.7 0.6534148
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6 10 0.1 150 0.9 0.6679453
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6 10 0.2 50 0.7 0.6833106
6 10 0.2 50 0.8 0.6848489
6 10 0.2 50 0.9 0.6849139
6 10 0.2 50 1.0 0.6865352
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6 10 0.2 150 0.9 0.6737643
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6 10 0.3 50 0.7 0.6848673
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6 10 0.3 50 0.9 0.6855448
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6 10 0.3 100 0.7 0.6792270
6 10 0.3 100 0.8 0.6802144
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6 10 0.3 150 0.7 0.6618491
6 10 0.3 150 0.8 0.6714346
6 10 0.3 150 0.9 0.6762072
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6 10 0.4 50 0.7 0.6849644
6 10 0.4 50 0.8 0.6857636
6 10 0.4 50 0.9 0.6858098
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6 10 0.4 100 0.7 0.6803238
6 10 0.4 100 0.8 0.6817646
6 10 0.4 100 0.9 0.6825858
6 10 0.4 100 1.0 0.6826382
6 10 0.4 150 0.7 0.6635773
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6 10 0.4 150 0.9 0.6773382
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6 10 0.5 50 0.7 0.6848764
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6 10 0.5 50 1.0 0.6872206
6 10 0.5 100 0.7 0.6815102
6 10 0.5 100 0.8 0.6824430
6 10 0.5 100 0.9 0.6830663
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6 10 0.5 150 0.8 0.6732858
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6 10 0.5 150 1.0 0.6800828
6 10 0.6 50 0.7 0.6848478
6 10 0.6 50 0.8 0.6857673
6 10 0.6 50 0.9 0.6850607
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6 10 0.6 100 0.7 0.6813360
6 10 0.6 100 0.8 0.6826713
6 10 0.6 100 0.9 0.6833282
6 10 0.6 100 1.0 0.6828132
6 10 0.6 150 0.7 0.6649906
6 10 0.6 150 0.8 0.6735891
6 10 0.6 150 0.9 0.6785107
6 10 0.6 150 1.0 0.6800013
6 10 0.7 50 0.7 0.6851910
6 10 0.7 50 0.8 0.6853738
6 10 0.7 50 0.9 0.6853930
6 10 0.7 50 1.0 0.6860383
6 10 0.7 100 0.7 0.6817957
6 10 0.7 100 0.8 0.6826959
6 10 0.7 100 0.9 0.6834013
6 10 0.7 100 1.0 0.6828476
6 10 0.7 150 0.7 0.6657564
6 10 0.7 150 0.8 0.6741803
6 10 0.7 150 0.9 0.6788473
6 10 0.7 150 1.0 0.6794988
6 10 0.8 50 0.7 0.6848133
6 10 0.8 50 0.8 0.6852163
6 10 0.8 50 0.9 0.6849904
6 10 0.8 50 1.0 0.6856171
6 10 0.8 100 0.7 0.6822651
6 10 0.8 100 0.8 0.6825848
6 10 0.8 100 0.9 0.6832538
6 10 0.8 100 1.0 0.6819740
6 10 0.8 150 0.7 0.6662588
6 10 0.8 150 0.8 0.6742240
6 10 0.8 150 0.9 0.6790967
6 10 0.8 150 1.0 0.6791401
6 10 0.9 50 0.7 0.6844932
6 10 0.9 50 0.8 0.6848488
6 10 0.9 50 0.9 0.6848179
6 10 0.9 50 1.0 0.6845067
6 10 0.9 100 0.7 0.6819226
6 10 0.9 100 0.8 0.6831226
6 10 0.9 100 0.9 0.6832891
6 10 0.9 100 1.0 0.6815188
6 10 0.9 150 0.7 0.6659969
6 10 0.9 150 0.8 0.6742377
6 10 0.9 150 0.9 0.6791359
6 10 0.9 150 1.0 0.6781489
6 10 1.0 50 0.7 0.6844464
6 10 1.0 50 0.8 0.6843285
6 10 1.0 50 0.9 0.6843639
6 10 1.0 50 1.0 0.6810106
6 10 1.0 100 0.7 0.6823986
6 10 1.0 100 0.8 0.6831334
6 10 1.0 100 0.9 0.6837060
6 10 1.0 100 1.0 0.6779725
6 10 1.0 150 0.7 0.6663187
6 10 1.0 150 0.8 0.6745562
6 10 1.0 150 0.9 0.6792473
6 10 1.0 150 1.0 0.6751654
Sens Spec
0.5854853 0.6720869
0.5854862 0.6749236
0.5883159 0.6772663
0.5857990 0.6801705
0.5923909 0.6576026
0.5938086 0.6591202
0.5965546 0.6602416
0.5944354 0.6606045
0.5854124 0.6386662
0.5890174 0.6474422
0.5938867 0.6524571
0.5960824 0.6558224
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0.5982791 0.6772005
0.6062078 0.6593854
0.6050279 0.6606719
0.6076196 0.6615298
0.6108390 0.6616953
0.5972608 0.6396895
0.6004020 0.6489943
0.6065252 0.6515673
0.6090341 0.6543713
0.5987500 0.6748918
0.5989860 0.6759473
0.5965543 0.6776956
0.5978873 0.6781906
0.6077758 0.6594525
0.6068353 0.6604744
0.6090320 0.6616292
0.6085635 0.6601453
0.5974124 0.6390624
0.6020482 0.6483664
0.6097446 0.6513692
0.6104480 0.6526222
0.5978058 0.6756183
0.5956897 0.6780584
0.5961603 0.6794113
0.5984382 0.6764424
0.6060519 0.6591875
0.6075387 0.6593865
0.6098157 0.6608051
0.6095807 0.6586599
0.5973355 0.6397213
0.6036974 0.6489272
0.6096637 0.6515014
0.6109955 0.6520938
0.5971805 0.6756845
0.5969474 0.6775305
0.5961612 0.6779259
0.5981235 0.6761788
0.6081708 0.6622232
0.6081667 0.6604748
0.6102081 0.6621907
0.6098169 0.6581646
0.5981189 0.6400849
0.6036168 0.6482668
0.6092719 0.6521945
0.6119386 0.6504777
0.5971789 0.6746946
0.5964761 0.6786854
0.5954553 0.6808965
0.5951410 0.6751547
0.6068343 0.6595507
0.6074606 0.6607389
0.6107584 0.6609365
0.6095025 0.6567465
0.5980408 0.6401845
0.6062069 0.6483996
0.6102912 0.6520958
0.6123313 0.6489270
0.5949798 0.6770033
0.5952981 0.6781587
0.5943570 0.6795115
0.5938861 0.6719222
0.6084811 0.6613649
0.6081701 0.6599144
0.6075411 0.6617618
0.6088776 0.6510712
0.5995325 0.6399199
0.6072274 0.6472123
0.6115455 0.6528216
0.6105289 0.6445067
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 = 6, eta
= 0.01, gamma = 0, colsample_bytree = 0.7, min_child_weight = 150
and subsample = 1.
Support Vector Machines with Radial Basis Function Kernel
4305 samples
91 predictor
2 classes: 'Not_D_and_inf_Q1', 'Not_D_and_sup_Q1'
Pre-processing: centered (188), scaled (188)
Resampling: Cross-Validated (5 fold, repeated 10 times)
Summary of sample sizes: 3444, 3444, 3444, 3445, 3443, 3444, ...
Resampling results across tuning parameters:
C ROC Sens Spec
2.500000e-01 0.6845732 0.18500726 0.9394922
5.000000e-01 0.6845750 0.18242057 0.9410422
1.000000e+00 0.6840859 0.17991323 0.9422641
2.000000e+00 0.6798961 0.16397684 0.9493251
4.000000e+00 0.6703732 0.14812722 0.9539767
8.000000e+00 0.6566924 0.12104184 0.9613335
1.600000e+01 0.6407620 0.09100085 0.9716467
3.200000e+01 0.6284837 0.06805521 0.9777494
6.400000e+01 0.6192433 0.06259466 0.9794860
1.280000e+02 0.6191579 0.06475330 0.9776136
2.560000e+02 0.6194775 0.06690340 0.9770227
5.120000e+02 0.6217497 0.06459543 0.9777956
1.024000e+03 0.6177933 0.06038327 0.9787580
2.048000e+03 0.6197161 0.05951205 0.9813836
4.096000e+03 0.6191493 0.06151835 0.9799168
8.192000e+03 0.6175882 0.06550064 0.9769857
1.638400e+04 0.6181877 0.06298648 0.9787567
3.276800e+04 0.6189728 0.06439820 0.9785556
6.553600e+04 0.6195841 0.06335973 0.9792230
1.310720e+05 0.6194290 0.06180079 0.9789008
2.621440e+05 0.6198103 0.05897868 0.9798427
5.242880e+05 0.6189223 0.06078682 0.9802061
1.048576e+06 0.6206165 0.05781581 0.9808264
2.097152e+06 0.6201163 0.05771642 0.9814124
4.194304e+06 0.6181878 0.06176838 0.9784390
8.388608e+06 0.6194077 0.06167317 0.9779674
1.677722e+07 0.6198161 0.06217771 0.9791928
3.355443e+07 0.6196947 0.06185587 0.9788317
6.710886e+07 0.6202174 0.06380287 0.9792079
1.342177e+08 0.6188412 0.06242749 0.9793425
2.684355e+08 0.6176711 0.05789675 0.9808018
5.368709e+08 0.6196809 0.05965965 0.9802059
1.073742e+09 0.6201879 0.06638327 0.9778590
2.147484e+09 0.6199448 0.06334543 0.9796292
4.294967e+09 0.6190879 0.05958005 0.9802028
8.589935e+09 0.6188724 0.06061197 0.9798608
1.717987e+10 0.6185836 0.06317593 0.9782026
3.435974e+10 0.6195006 0.05896523 0.9804261
6.871948e+10 0.6205110 0.06532187 0.9793169
1.374390e+11 0.6190431 0.06202322 0.9794408
2.748779e+11 0.6185959 0.06370787 0.9791893
5.497558e+11 0.6196803 0.06260614 0.9790483
1.099512e+12 0.6189025 0.06194678 0.9798426
2.199023e+12 0.6180484 0.05994673 0.9797927
4.398047e+12 0.6201210 0.05840562 0.9803555
8.796093e+12 0.6211872 0.06125993 0.9797466
1.759219e+13 0.6184832 0.06168053 0.9803620
3.518437e+13 0.6186460 0.06325937 0.9790679
7.036874e+13 0.6193463 0.06602495 0.9777952
1.407375e+14 0.6189965 0.05886874 0.9806771
2.814750e+14 0.6204431 0.06429123 0.9779287
5.629500e+14 0.6191002 0.06043461 0.9795445
1.125900e+15 0.6196932 0.06164665 0.9792811
2.251800e+15 0.6212284 0.06310931 0.9794839
4.503600e+15 0.6194147 0.06195108 0.9796156
9.007199e+15 0.6189499 0.06304764 0.9789768
1.801440e+16 0.6190483 0.06269724 0.9789688
3.602880e+16 0.6195203 0.05914738 0.9795222
7.205759e+16 0.6181346 0.06159151 0.9791887
1.441152e+17 0.6199934 0.05990134 0.9793019
2.882304e+17 0.6211486 0.05887301 0.9810091
5.764608e+17 0.6200023 0.06106486 0.9781537
1.152922e+18 0.6219580 0.06558044 0.9781814
2.305843e+18 0.6202328 0.06252719 0.9786332
4.611686e+18 0.6184795 0.06339078 0.9791044
9.223372e+18 0.6194937 0.05795243 0.9802058
1.844674e+19 0.6202671 0.06154891 0.9788177
3.689349e+19 0.6196105 0.06490157 0.9783643
7.378698e+19 0.6204483 0.06040686 0.9794684
1.475740e+20 0.6193828 0.06061105 0.9787396
2.951479e+20 0.6204511 0.06179469 0.9800144
5.902958e+20 0.6183741 0.06116540 0.9787963
1.180592e+21 0.6209400 0.06411523 0.9785145
2.361183e+21 0.6193779 0.06466960 0.9778560
4.722366e+21 0.6182601 0.06461721 0.9777089
9.444733e+21 0.6193490 0.06315944 0.9796675
1.888947e+22 0.6190460 0.06270551 0.9792769
3.777893e+22 0.6200651 0.06220965 0.9794672
7.555786e+22 0.6216710 0.06198494 0.9789899
1.511157e+23 0.6203920 0.05768892 0.9811646
3.022315e+23 0.6190263 0.06025668 0.9797500
6.044629e+23 0.6203105 0.06212828 0.9793594
1.208926e+24 0.6187173 0.06250836 0.9786330
2.417852e+24 0.6196223 0.05982369 0.9796821
4.835703e+24 0.6187729 0.05975876 0.9795862
9.671407e+24 0.6203110 0.06023354 0.9801699
1.934281e+25 0.6195157 0.06502927 0.9784628
3.868563e+25 0.6186238 0.06126218 0.9789168
7.737125e+25 0.6184958 0.06045091 0.9782006
1.547425e+26 0.6190884 0.06342162 0.9789310
3.094850e+26 0.6181873 0.06142845 0.9784399
6.189700e+26 0.6189298 0.06062272 0.9786674
1.237940e+27 0.6187053 0.06531791 0.9788524
2.475880e+27 0.6203114 0.06206026 0.9789872
4.951760e+27 0.6192489 0.06124180 0.9790311
9.903520e+27 0.6174195 0.06008044 0.9802894
1.980704e+28 0.6193041 0.06138461 0.9798124
3.961408e+28 0.6196082 0.06496835 0.9781007
7.922816e+28 0.6190864 0.06123360 0.9788435
1.584563e+29 0.6188447 0.06135820 0.9797634
Tuning parameter 'sigma' was held constant at a value of 0.00345505
ROC was used to select the optimal model using the largest value.
The final values used for the model were sigma = 0.00345505 and C = 0.5.
glmnet
4305 samples
91 predictor
2 classes: 'Not_D_and_inf_Q1', 'Not_D_and_sup_Q1'
Pre-processing: centered (188), scaled (188)
Resampling: Cross-Validated (5 fold, repeated 10 times)
Summary of sample sizes: 3445, 3444, 3444, 3443, 3444, 3444, ...
Resampling results across tuning parameters:
alpha lambda ROC Sens Spec
0.1000000 0.0001622919 0.6820664 2.865147e-01 0.9014490
0.1000000 0.0001777873 0.6820948 2.865935e-01 0.9014490
0.1000000 0.0001947622 0.6821287 2.865935e-01 0.9015151
0.1000000 0.0002133579 0.6821721 2.866719e-01 0.9015810
0.1000000 0.0002337290 0.6822207 2.865935e-01 0.9015480
0.1000000 0.0002560452 0.6822747 2.863582e-01 0.9016140
0.1000000 0.0002804921 0.6823467 2.863582e-01 0.9017129
0.1000000 0.0003072731 0.6824176 2.861229e-01 0.9016800
0.1000000 0.0003366112 0.6824942 2.861226e-01 0.9017460
0.1000000 0.0003687504 0.6825960 2.858876e-01 0.9019769
0.1000000 0.0004039582 0.6826886 2.858876e-01 0.9021747
0.1000000 0.0004425276 0.6827959 2.855739e-01 0.9023067
0.1000000 0.0004847796 0.6829189 2.854167e-01 0.9023727
0.1000000 0.0005310658 0.6830447 2.853383e-01 0.9027356
0.1000000 0.0005817713 0.6831674 2.848671e-01 0.9029336
0.1000000 0.0006373181 0.6833177 2.846312e-01 0.9031315
0.1000000 0.0006981684 0.6834787 2.843949e-01 0.9036263
0.1000000 0.0007648286 0.6836411 2.840806e-01 0.9038902
0.1000000 0.0008378535 0.6838102 2.840025e-01 0.9042201
0.1000000 0.0009178507 0.6839830 2.832960e-01 0.9043851
0.1000000 0.0010054860 0.6841938 2.831394e-01 0.9048141
0.1000000 0.0011014885 0.6843950 2.825895e-01 0.9052761
0.1000000 0.0012066573 0.6846072 2.820392e-01 0.9057381
0.1000000 0.0013218674 0.6848117 2.820389e-01 0.9061339
0.1000000 0.0014480777 0.6850369 2.820389e-01 0.9063978
0.1000000 0.0015863384 0.6852791 2.812537e-01 0.9068925
0.1000000 0.0017378000 0.6855403 2.812543e-01 0.9072555
0.1000000 0.0019037230 0.6857617 2.799981e-01 0.9075854
0.1000000 0.0020854881 0.6859973 2.798419e-01 0.9081793
0.1000000 0.0022846079 0.6862474 2.792145e-01 0.9084765
0.1000000 0.0025027394 0.6864829 2.782733e-01 0.9091363
0.1000000 0.0027416978 0.6867506 2.764675e-01 0.9096642
0.1000000 0.0030034717 0.6870065 2.752892e-01 0.9102910
0.1000000 0.0032902395 0.6872553 2.745836e-01 0.9107860
0.1000000 0.0036043875 0.6874813 2.734074e-01 0.9112809
0.1000000 0.0039485299 0.6877504 2.717595e-01 0.9121717
0.1000000 0.0043255306 0.6879966 2.695612e-01 0.9132604
0.1000000 0.0047385269 0.6882488 2.679914e-01 0.9140521
0.1000000 0.0051909556 0.6884544 2.670474e-01 0.9149759
0.1000000 0.0056865816 0.6886767 2.652413e-01 0.9157347
0.1000000 0.0062295293 0.6889413 2.628856e-01 0.9164276
0.1000000 0.0068243170 0.6892319 2.608436e-01 0.9172524
0.1000000 0.0074758943 0.6895229 2.582538e-01 0.9181102
0.1000000 0.0081896834 0.6898021 2.550369e-01 0.9193311
0.1000000 0.0089716242 0.6901055 2.522124e-01 0.9203541
0.1000000 0.0098282238 0.6904228 2.499348e-01 0.9213109
0.1000000 0.0107666104 0.6907349 2.467145e-01 0.9225977
0.1000000 0.0117945929 0.6910564 2.446744e-01 0.9237853
0.1000000 0.0129207260 0.6913766 2.422418e-01 0.9245771
0.1000000 0.0141543809 0.6917032 2.402019e-01 0.9260948
0.1000000 0.0155058236 0.6920006 2.372185e-01 0.9272167
0.1000000 0.0169863003 0.6922665 2.350187e-01 0.9287012
0.1000000 0.0186081312 0.6925076 2.310931e-01 0.9308129
0.1000000 0.0203848124 0.6927917 2.282671e-01 0.9322978
0.1000000 0.0223311290 0.6930042 2.248136e-01 0.9341784
0.1000000 0.0244632774 0.6932080 2.215171e-01 0.9359269
0.1000000 0.0267990007 0.6933583 2.176715e-01 0.9376754
0.1000000 0.0293577360 0.6935407 2.131184e-01 0.9399517
0.1000000 0.0321607762 0.6936890 2.088001e-01 0.9424920
0.1000000 0.0352314471 0.6938403 2.039330e-01 0.9449997
0.1000000 0.0385953019 0.6939510 1.974967e-01 0.9478372
0.1000000 0.0422803333 0.6940230 1.925518e-01 0.9503446
0.1000000 0.0463172070 0.6939800 1.859568e-01 0.9533801
0.1000000 0.0507395164 0.6938487 1.790471e-01 0.9564483
0.1000000 0.0555840623 0.6935337 1.708056e-01 0.9595169
0.1000000 0.0608911595 0.6932088 1.620923e-01 0.9623871
0.1000000 0.0667049717 0.6927322 1.527522e-01 0.9656535
0.1000000 0.0730738795 0.6920587 1.416066e-01 0.9687877
0.1000000 0.0800508827 0.6912479 1.300664e-01 0.9722188
0.1000000 0.0876940415 0.6902117 1.195467e-01 0.9754850
0.1000000 0.0960669596 0.6889593 1.072228e-01 0.9780914
0.1000000 0.1052393135 0.6875139 9.466111e-02 0.9814897
0.1000000 0.1152874324 0.6856918 8.265061e-02 0.9843271
0.1000000 0.1262949331 0.6836118 7.009325e-02 0.9873299
0.1090909 0.0001622919 0.6819554 2.863585e-01 0.9012511
0.1090909 0.0001777873 0.6819880 2.864372e-01 0.9012511
0.1090909 0.0001947622 0.6820331 2.862801e-01 0.9013171
0.1090909 0.0002133579 0.6820845 2.862801e-01 0.9013831
0.1090909 0.0002337290 0.6821481 2.862013e-01 0.9013830
0.1090909 0.0002560452 0.6822229 2.861229e-01 0.9014160
0.1090909 0.0002804921 0.6823037 2.861229e-01 0.9015479
0.1090909 0.0003072731 0.6823870 2.859657e-01 0.9014820
0.1090909 0.0003366112 0.6824811 2.857304e-01 0.9017790
0.1090909 0.0003687504 0.6825750 2.859663e-01 0.9019767
0.1090909 0.0004039582 0.6826916 2.857310e-01 0.9021417
0.1090909 0.0004425276 0.6828101 2.855739e-01 0.9021417
0.1090909 0.0004847796 0.6829358 2.852598e-01 0.9024387
0.1090909 0.0005310658 0.6830654 2.851027e-01 0.9027026
0.1090909 0.0005817713 0.6831982 2.846318e-01 0.9028676
0.1090909 0.0006373181 0.6833566 2.845524e-01 0.9032965
0.1090909 0.0006981684 0.6835209 2.842381e-01 0.9037913
0.1090909 0.0007648286 0.6836886 2.840025e-01 0.9039892
0.1090909 0.0008378535 0.6838457 2.836103e-01 0.9042201
0.1090909 0.0009178507 0.6840553 2.833747e-01 0.9046162
0.1090909 0.0010054860 0.6842603 2.830610e-01 0.9050121
0.1090909 0.0011014885 0.6844600 2.822751e-01 0.9054411
0.1090909 0.0012066573 0.6846823 2.820389e-01 0.9059030
0.1090909 0.0013218674 0.6848902 2.820392e-01 0.9061998
0.1090909 0.0014480777 0.6851147 2.814896e-01 0.9066287
0.1090909 0.0015863384 0.6853911 2.814111e-01 0.9070574
0.1090909 0.0017378000 0.6856283 2.811762e-01 0.9073215
0.1090909 0.0019037230 0.6858487 2.799981e-01 0.9078164
0.1090909 0.0020854881 0.6861084 2.797635e-01 0.9082124
0.1090909 0.0022846079 0.6863501 2.785083e-01 0.9088064
0.1090909 0.0025027394 0.6866052 2.773318e-01 0.9094333
0.1090909 0.0027416978 0.6868789 2.761532e-01 0.9097962
0.1090909 0.0030034717 0.6871362 2.748189e-01 0.9105220
0.1090909 0.0032902395 0.6873659 2.737999e-01 0.9110830
0.1090909 0.0036043875 0.6876017 2.725441e-01 0.9116439
0.1090909 0.0039485299 0.6878667 2.706608e-01 0.9126337
0.1090909 0.0043255306 0.6881287 2.686191e-01 0.9135573
0.1090909 0.0047385269 0.6883375 2.673627e-01 0.9145471
0.1090909 0.0051909556 0.6885560 2.661840e-01 0.9152069
0.1090909 0.0056865816 0.6887848 2.642202e-01 0.9160317
0.1090909 0.0062295293 0.6890950 2.614717e-01 0.9168564
0.1090909 0.0068243170 0.6893893 2.594315e-01 0.9174833
0.1090909 0.0074758943 0.6896589 2.565274e-01 0.9186382
0.1090909 0.0081896834 0.6899688 2.538604e-01 0.9196282
0.1090909 0.0089716242 0.6902879 2.505629e-01 0.9208818
0.1090909 0.0098282238 0.6906030 2.482856e-01 0.9219380
0.1090909 0.0107666104 0.6909394 2.458515e-01 0.9230926
0.1090909 0.0117945929 0.6912675 2.433407e-01 0.9242471
0.1090909 0.0129207260 0.6915843 2.416140e-01 0.9253689
0.1090909 0.0141543809 0.6919118 2.386324e-01 0.9268536
0.1090909 0.0155058236 0.6921807 2.355689e-01 0.9280085
0.1090909 0.0169863003 0.6924574 2.332138e-01 0.9300541
0.1090909 0.0186081312 0.6927160 2.296795e-01 0.9315059
0.1090909 0.0203848124 0.6929933 2.266966e-01 0.9333204
0.1090909 0.0223311290 0.6931857 2.230085e-01 0.9350692
0.1090909 0.0244632774 0.6933875 2.201828e-01 0.9367186
0.1090909 0.0267990007 0.6935579 2.160222e-01 0.9390940
0.1090909 0.0293577360 0.6937167 2.111555e-01 0.9413374
0.1090909 0.0321607762 0.6938651 2.062100e-01 0.9440758
0.1090909 0.0352314471 0.6940018 2.005574e-01 0.9467153
0.1090909 0.0385953019 0.6940778 1.941995e-01 0.9490908
0.1090909 0.0422803333 0.6940771 1.888609e-01 0.9524231
0.1090909 0.0463172070 0.6939767 1.823440e-01 0.9551288
0.1090909 0.0507395164 0.6936720 1.744941e-01 0.9581641
0.1090909 0.0555840623 0.6933633 1.657036e-01 0.9612985
0.1090909 0.0608911595 0.6929127 1.562844e-01 0.9639707
0.1090909 0.0667049717 0.6922473 1.464731e-01 0.9674020
0.1090909 0.0730738795 0.6914637 1.346194e-01 0.9707341
0.1090909 0.0800508827 0.6904836 1.238648e-01 0.9740333
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0.9909091 0.0129207260 0.6841686 1.226833e-01 0.9702393
0.9909091 0.0141543809 0.6811292 1.093377e-01 0.9737036
0.9909091 0.0155058236 0.6777788 9.591447e-02 0.9764422
0.9909091 0.0169863003 0.6743517 8.241593e-02 0.9797084
0.9909091 0.0186081312 0.6710091 6.695507e-02 0.9834698
0.9909091 0.0203848124 0.6672957 5.533889e-02 0.9871981
0.9909091 0.0223311290 0.6632861 4.372333e-02 0.9897716
0.9909091 0.0244632774 0.6593301 3.029767e-02 0.9926749
0.9909091 0.0267990007 0.6555860 2.134877e-02 0.9947867
0.9909091 0.0293577360 0.6518693 1.357789e-02 0.9962714
0.9909091 0.0321607762 0.6477874 8.475838e-03 0.9974923
0.9909091 0.0352314471 0.6433683 4.866759e-03 0.9984822
0.9909091 0.0385953019 0.6382367 2.747877e-03 0.9990761
0.9909091 0.0422803333 0.6319173 1.804848e-03 0.9991750
0.9909091 0.0463172070 0.6240009 1.176779e-03 0.9992740
0.9909091 0.0507395164 0.6153215 5.493284e-04 0.9995050
0.9909091 0.0555840623 0.6063874 1.571715e-04 0.9997690
0.9909091 0.0608911595 0.5946502 0.000000e+00 0.9999670
0.9909091 0.0667049717 0.5747619 0.000000e+00 1.0000000
0.9909091 0.0730738795 0.5123101 0.000000e+00 1.0000000
0.9909091 0.0800508827 0.5000000 0.000000e+00 1.0000000
0.9909091 0.0876940415 0.5000000 0.000000e+00 1.0000000
0.9909091 0.0960669596 0.5000000 0.000000e+00 1.0000000
0.9909091 0.1052393135 0.5000000 0.000000e+00 1.0000000
0.9909091 0.1152874324 0.5000000 0.000000e+00 1.0000000
0.9909091 0.1262949331 0.5000000 0.000000e+00 1.0000000
1.0000000 0.0001622919 0.6830781 2.840809e-01 0.9033624
1.0000000 0.0001777873 0.6832334 2.836097e-01 0.9035603
1.0000000 0.0001947622 0.6834030 2.832957e-01 0.9038573
1.0000000 0.0002133579 0.6836207 2.831391e-01 0.9041542
1.0000000 0.0002337290 0.6838510 2.824323e-01 0.9045829
1.0000000 0.0002560452 0.6841087 2.815677e-01 0.9048799
1.0000000 0.0002804921 0.6843732 2.814896e-01 0.9056387
1.0000000 0.0003072731 0.6846554 2.808612e-01 0.9057377
1.0000000 0.0003366112 0.6849512 2.805478e-01 0.9063975
1.0000000 0.0003687504 0.6852519 2.796072e-01 0.9067934
1.0000000 0.0004039582 0.6855544 2.790579e-01 0.9072883
1.0000000 0.0004425276 0.6858632 2.782730e-01 0.9079814
1.0000000 0.0004847796 0.6861781 2.772521e-01 0.9082454
1.0000000 0.0005310658 0.6865217 2.763881e-01 0.9089712
1.0000000 0.0005817713 0.6868479 2.755251e-01 0.9097631
1.0000000 0.0006373181 0.6871274 2.744267e-01 0.9102580
1.0000000 0.0006981684 0.6873956 2.724638e-01 0.9108519
1.0000000 0.0007648286 0.6876738 2.716782e-01 0.9114127
1.0000000 0.0008378535 0.6879504 2.697925e-01 0.9126994
1.0000000 0.0009178507 0.6882110 2.685370e-01 0.9133592
1.0000000 0.0010054860 0.6885102 2.668096e-01 0.9141843
1.0000000 0.0011014885 0.6888182 2.646908e-01 0.9148443
1.0000000 0.0012066573 0.6891647 2.635912e-01 0.9162630
1.0000000 0.0013218674 0.6895317 2.616285e-01 0.9174839
1.0000000 0.0014480777 0.6899192 2.595096e-01 0.9181436
1.0000000 0.0015863384 0.6903202 2.565249e-01 0.9191663
1.0000000 0.0017378000 0.6907394 2.539348e-01 0.9205192
1.0000000 0.0019037230 0.6911691 2.526024e-01 0.9215754
1.0000000 0.0020854881 0.6915814 2.491471e-01 0.9231260
1.0000000 0.0022846079 0.6919774 2.464792e-01 0.9242805
1.0000000 0.0025027394 0.6923823 2.442828e-01 0.9253364
1.0000000 0.0027416978 0.6927792 2.410638e-01 0.9270519
1.0000000 0.0030034717 0.6931858 2.377672e-01 0.9288339
1.0000000 0.0032902395 0.6935413 2.340775e-01 0.9302526
1.0000000 0.0036043875 0.6938982 2.311718e-01 0.9324962
1.0000000 0.0039485299 0.6942508 2.276381e-01 0.9343108
1.0000000 0.0043255306 0.6945388 2.242618e-01 0.9363894
1.0000000 0.0047385269 0.6947438 2.197869e-01 0.9385667
1.0000000 0.0051909556 0.6948815 2.150774e-01 0.9408431
1.0000000 0.0056865816 0.6948705 2.103681e-01 0.9428226
1.0000000 0.0062295293 0.6947191 2.037758e-01 0.9457593
1.0000000 0.0068243170 0.6942727 1.974961e-01 0.9490586
1.0000000 0.0074758943 0.6937060 1.887828e-01 0.9520939
1.0000000 0.0081896834 0.6928746 1.803838e-01 0.9548981
1.0000000 0.0089716242 0.6918253 1.719058e-01 0.9578345
1.0000000 0.0098282238 0.6904607 1.598163e-01 0.9607705
1.0000000 0.0107666104 0.6886599 1.479620e-01 0.9639046
1.0000000 0.0117945929 0.6864535 1.346948e-01 0.9669399
1.0000000 0.0129207260 0.6838824 1.208776e-01 0.9706682
1.0000000 0.0141543809 0.6808200 1.082390e-01 0.9739014
1.0000000 0.0155058236 0.6774205 9.403026e-02 0.9766401
1.0000000 0.0169863003 0.6740227 8.084607e-02 0.9799394
1.0000000 0.0186081312 0.6706336 6.569955e-02 0.9837997
1.0000000 0.0203848124 0.6668977 5.455396e-02 0.9873301
1.0000000 0.0223311290 0.6628692 4.231002e-02 0.9901674
1.0000000 0.0244632774 0.6589362 2.935557e-02 0.9928399
1.0000000 0.0267990007 0.6552137 2.079914e-02 0.9949515
1.0000000 0.0293577360 0.6514816 1.318512e-02 0.9963374
1.0000000 0.0321607762 0.6473570 8.005249e-03 0.9975254
1.0000000 0.0352314471 0.6429164 4.631465e-03 0.9986142
1.0000000 0.0385953019 0.6376575 2.747877e-03 0.9990761
1.0000000 0.0422803333 0.6312157 1.647985e-03 0.9991750
1.0000000 0.0463172070 0.6230157 1.098348e-03 0.9992740
1.0000000 0.0507395164 0.6146817 4.708970e-04 0.9995380
1.0000000 0.0555840623 0.6057076 1.571715e-04 0.9998020
1.0000000 0.0608911595 0.5936464 0.000000e+00 0.9999670
1.0000000 0.0667049717 0.5721188 0.000000e+00 1.0000000
1.0000000 0.0730738795 0.5082284 0.000000e+00 1.0000000
1.0000000 0.0800508827 0.5000000 0.000000e+00 1.0000000
1.0000000 0.0876940415 0.5000000 0.000000e+00 1.0000000
1.0000000 0.0960669596 0.5000000 0.000000e+00 1.0000000
1.0000000 0.1052393135 0.5000000 0.000000e+00 1.0000000
1.0000000 0.1152874324 0.5000000 0.000000e+00 1.0000000
1.0000000 0.1262949331 0.5000000 0.000000e+00 1.0000000
ROC was used to select the optimal model using the largest value.
The final values used for the model were alpha = 0.9545455 and lambda
= 0.005686582.
Visualization of Results
Let us apply the resamples ()train ()Chapter 17 ). 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 0.6347256 0.6645601 0.6767909 0.6742330 0.6869602 0.7125607 0
xgb 0.6464376 0.6722643 0.6839772 0.6822150 0.6923111 0.7161975 0
svm 0.6284400 0.6761443 0.6871079 0.6845750 0.6959975 0.7184689 0
glmnet 0.6553744 0.6784063 0.6950676 0.6949193 0.7065919 0.7552310 0
Sens
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
rf 0.03921569 0.05490196 0.06090011 0.0629560 0.07086614 0.09019608 0
xgb 0.52941176 0.58921569 0.61176471 0.6138996 0.63823529 0.67058824 0
svm 0.12941176 0.16470588 0.18110236 0.1824206 0.20313803 0.24409449 0
glmnet 0.16862745 0.19607843 0.20784314 0.2127219 0.22745098 0.25882353 0
Spec
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
rf 0.9752475 0.9818482 0.9851730 0.9851537 0.9884488 0.9983498 0
xgb 0.6056106 0.6456271 0.6584158 0.6555585 0.6662541 0.7095710 0
svm 0.8844884 0.9311335 0.9405941 0.9410422 0.9517531 0.9752475 0
glmnet 0.9075908 0.9356436 0.9438944 0.9416679 0.9484541 0.9636964 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_sah.rda" )load ("../data/out/df_test_sah.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)
