This chapter presents the results obtained with the classifier trained in Chapter 11 .
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✔ 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 11 .
load ("../data/out/estim/v3/grid_search_rf_mhi-3.rda" )load ("../data/out/estim/v3/grid_search_xgb_mhi-3.rda" )load ("../data/out/estim/v3/svm_model_mhi-3.rda" )load ("../data/out/estim/v3/glmnet_model_mhi-3.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, 3388, 3388, 3388, ...
Resampling results across tuning parameters:
mtry min.node.size ROC Sens Spec
3 50 0.6967044 0.026930693 0.9968682
3 75 0.6964931 0.017029703 0.9980465
3 100 0.6949282 0.010891089 0.9986667
3 150 0.6926354 0.001980198 0.9995659
4 50 0.6977572 0.040396040 0.9942326
4 75 0.6964125 0.030594059 0.9964651
4 100 0.6952454 0.021683168 0.9973643
4 150 0.6935498 0.008415842 0.9986667
5 50 0.6977811 0.053960396 0.9923101
5 75 0.6964825 0.040099010 0.9946977
5 100 0.6958285 0.032079208 0.9963101
5 150 0.6944293 0.016732673 0.9981395
6 50 0.6972308 0.064950495 0.9897054
6 75 0.6973091 0.048811881 0.9928062
6 100 0.6962947 0.039405941 0.9949457
6 150 0.6938387 0.023861386 0.9974884
7 50 0.6970972 0.072079208 0.9879380
7 75 0.6966449 0.058712871 0.9912248
7 100 0.6962132 0.045742574 0.9937984
7 150 0.6940355 0.029009901 0.9967752
8 50 0.6975303 0.079603960 0.9862326
8 75 0.6967657 0.067326733 0.9897364
8 100 0.6952062 0.051188119 0.9922171
8 150 0.6942851 0.034653465 0.9959690
9 50 0.6971720 0.086138614 0.9845581
9 75 0.6969388 0.071584158 0.9880310
9 100 0.6955905 0.058613861 0.9908837
9 150 0.6945646 0.038415842 0.9948837
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, 3388, 3388, 3388, 3388, 3388, ...
Resampling results across tuning parameters:
max_depth gamma colsample_bytree min_child_weight subsample ROC
3 0 0.1 50 0.7 0.6982884
3 0 0.1 50 0.8 0.7001217
3 0 0.1 50 0.9 0.7002225
3 0 0.1 50 1.0 0.7010249
3 0 0.1 100 0.7 0.6898058
3 0 0.1 100 0.8 0.6913149
3 0 0.1 100 0.9 0.6921893
3 0 0.1 100 1.0 0.6950756
3 0 0.1 150 0.7 0.6706863
3 0 0.1 150 0.8 0.6828389
3 0 0.1 150 0.9 0.6869460
3 0 0.1 150 1.0 0.6892014
3 0 0.2 50 0.7 0.7024852
3 0 0.2 50 0.8 0.7032523
3 0 0.2 50 0.9 0.7028777
3 0 0.2 50 1.0 0.7025066
3 0 0.2 100 0.7 0.6934762
3 0 0.2 100 0.8 0.6950273
3 0 0.2 100 0.9 0.6951939
3 0 0.2 100 1.0 0.6967344
3 0 0.2 150 0.7 0.6741377
3 0 0.2 150 0.8 0.6865386
3 0 0.2 150 0.9 0.6898124
3 0 0.2 150 1.0 0.6911543
3 0 0.3 50 0.7 0.7027275
3 0 0.3 50 0.8 0.7032153
3 0 0.3 50 0.9 0.7027684
3 0 0.3 50 1.0 0.7022737
3 0 0.3 100 0.7 0.6941701
3 0 0.3 100 0.8 0.6957050
3 0 0.3 100 0.9 0.6959434
3 0 0.3 100 1.0 0.6973392
3 0 0.3 150 0.7 0.6747538
3 0 0.3 150 0.8 0.6871758
3 0 0.3 150 0.9 0.6902970
3 0 0.3 150 1.0 0.6919057
3 0 0.4 50 0.7 0.7031143
3 0 0.4 50 0.8 0.7035687
3 0 0.4 50 0.9 0.7029121
3 0 0.4 50 1.0 0.7017924
3 0 0.4 100 0.7 0.6941219
3 0 0.4 100 0.8 0.6956787
3 0 0.4 100 0.9 0.6956491
3 0 0.4 100 1.0 0.6970741
3 0 0.4 150 0.7 0.6750485
3 0 0.4 150 0.8 0.6868382
3 0 0.4 150 0.9 0.6902193
3 0 0.4 150 1.0 0.6920367
3 0 0.5 50 0.7 0.7034334
3 0 0.5 50 0.8 0.7037806
3 0 0.5 50 0.9 0.7032585
3 0 0.5 50 1.0 0.7018476
3 0 0.5 100 0.7 0.6950429
3 0 0.5 100 0.8 0.6955126
3 0 0.5 100 0.9 0.6958207
3 0 0.5 100 1.0 0.6973358
3 0 0.5 150 0.7 0.6749876
3 0 0.5 150 0.8 0.6871471
3 0 0.5 150 0.9 0.6901962
3 0 0.5 150 1.0 0.6915583
3 0 0.6 50 0.7 0.7030559
3 0 0.6 50 0.8 0.7035314
3 0 0.6 50 0.9 0.7034112
3 0 0.6 50 1.0 0.7014702
3 0 0.6 100 0.7 0.6945163
3 0 0.6 100 0.8 0.6960178
3 0 0.6 100 0.9 0.6957271
3 0 0.6 100 1.0 0.6970094
3 0 0.6 150 0.7 0.6752834
3 0 0.6 150 0.8 0.6873009
3 0 0.6 150 0.9 0.6902049
3 0 0.6 150 1.0 0.6918028
3 0 0.7 50 0.7 0.7037268
3 0 0.7 50 0.8 0.7035892
3 0 0.7 50 0.9 0.7033137
3 0 0.7 50 1.0 0.7013199
3 0 0.7 100 0.7 0.6947925
3 0 0.7 100 0.8 0.6957057
3 0 0.7 100 0.9 0.6959790
3 0 0.7 100 1.0 0.6969586
3 0 0.7 150 0.7 0.6752199
3 0 0.7 150 0.8 0.6870582
3 0 0.7 150 0.9 0.6904056
3 0 0.7 150 1.0 0.6916415
3 0 0.8 50 0.7 0.7036618
3 0 0.8 50 0.8 0.7039715
3 0 0.8 50 0.9 0.7033092
3 0 0.8 50 1.0 0.7009675
3 0 0.8 100 0.7 0.6947328
3 0 0.8 100 0.8 0.6956297
3 0 0.8 100 0.9 0.6957203
3 0 0.8 100 1.0 0.6968095
3 0 0.8 150 0.7 0.6750533
3 0 0.8 150 0.8 0.6873560
3 0 0.8 150 0.9 0.6904754
3 0 0.8 150 1.0 0.6917274
3 0 0.9 50 0.7 0.7040300
3 0 0.9 50 0.8 0.7037169
3 0 0.9 50 0.9 0.7033699
3 0 0.9 50 1.0 0.7006661
3 0 0.9 100 0.7 0.6945403
3 0 0.9 100 0.8 0.6959205
3 0 0.9 100 0.9 0.6958854
3 0 0.9 100 1.0 0.6965812
3 0 0.9 150 0.7 0.6754482
3 0 0.9 150 0.8 0.6875507
3 0 0.9 150 0.9 0.6904316
3 0 0.9 150 1.0 0.6916550
3 0 1.0 50 0.7 0.7035350
3 0 1.0 50 0.8 0.7041045
3 0 1.0 50 0.9 0.7033055
3 0 1.0 50 1.0 0.7001937
3 0 1.0 100 0.7 0.6950235
3 0 1.0 100 0.8 0.6960448
3 0 1.0 100 0.9 0.6957753
3 0 1.0 100 1.0 0.6964528
3 0 1.0 150 0.7 0.6751789
3 0 1.0 150 0.8 0.6871428
3 0 1.0 150 0.9 0.6906159
3 0 1.0 150 1.0 0.6916117
3 5 0.1 50 0.7 0.6981793
3 5 0.1 50 0.8 0.6996132
3 5 0.1 50 0.9 0.7003048
3 5 0.1 50 1.0 0.7006642
3 5 0.1 100 0.7 0.6891705
3 5 0.1 100 0.8 0.6918817
3 5 0.1 100 0.9 0.6920737
3 5 0.1 100 1.0 0.6949617
3 5 0.1 150 0.7 0.6702246
3 5 0.1 150 0.8 0.6831319
3 5 0.1 150 0.9 0.6870251
3 5 0.1 150 1.0 0.6890171
3 5 0.2 50 0.7 0.7021926
3 5 0.2 50 0.8 0.7030749
3 5 0.2 50 0.9 0.7024772
3 5 0.2 50 1.0 0.7022987
3 5 0.2 100 0.7 0.6932322
3 5 0.2 100 0.8 0.6948188
3 5 0.2 100 0.9 0.6953143
3 5 0.2 100 1.0 0.6967817
3 5 0.2 150 0.7 0.6737909
3 5 0.2 150 0.8 0.6861451
3 5 0.2 150 0.9 0.6893077
3 5 0.2 150 1.0 0.6911540
3 5 0.3 50 0.7 0.7030526
3 5 0.3 50 0.8 0.7032781
3 5 0.3 50 0.9 0.7028334
3 5 0.3 50 1.0 0.7016530
3 5 0.3 100 0.7 0.6939771
3 5 0.3 100 0.8 0.6951546
3 5 0.3 100 0.9 0.6956076
3 5 0.3 100 1.0 0.6974451
3 5 0.3 150 0.7 0.6740763
3 5 0.3 150 0.8 0.6873131
3 5 0.3 150 0.9 0.6898817
3 5 0.3 150 1.0 0.6916726
3 5 0.4 50 0.7 0.7032041
3 5 0.4 50 0.8 0.7034122
3 5 0.4 50 0.9 0.7028887
3 5 0.4 50 1.0 0.7019613
3 5 0.4 100 0.7 0.6945523
3 5 0.4 100 0.8 0.6959182
3 5 0.4 100 0.9 0.6959274
3 5 0.4 100 1.0 0.6973006
3 5 0.4 150 0.7 0.6744833
3 5 0.4 150 0.8 0.6871999
3 5 0.4 150 0.9 0.6900674
3 5 0.4 150 1.0 0.6918235
3 5 0.5 50 0.7 0.7032937
3 5 0.5 50 0.8 0.7035102
3 5 0.5 50 0.9 0.7028789
3 5 0.5 50 1.0 0.7016374
3 5 0.5 100 0.7 0.6945558
3 5 0.5 100 0.8 0.6956887
3 5 0.5 100 0.9 0.6959429
3 5 0.5 100 1.0 0.6973577
3 5 0.5 150 0.7 0.6751620
3 5 0.5 150 0.8 0.6871488
3 5 0.5 150 0.9 0.6902568
3 5 0.5 150 1.0 0.6917739
3 5 0.6 50 0.7 0.7035748
3 5 0.6 50 0.8 0.7031309
3 5 0.6 50 0.9 0.7031520
3 5 0.6 50 1.0 0.7015792
3 5 0.6 100 0.7 0.6942476
3 5 0.6 100 0.8 0.6957854
3 5 0.6 100 0.9 0.6959120
3 5 0.6 100 1.0 0.6974090
3 5 0.6 150 0.7 0.6751549
3 5 0.6 150 0.8 0.6869522
3 5 0.6 150 0.9 0.6902451
3 5 0.6 150 1.0 0.6916624
3 5 0.7 50 0.7 0.7038757
3 5 0.7 50 0.8 0.7037938
3 5 0.7 50 0.9 0.7030202
3 5 0.7 50 1.0 0.7012224
3 5 0.7 100 0.7 0.6950638
3 5 0.7 100 0.8 0.6956934
3 5 0.7 100 0.9 0.6958262
3 5 0.7 100 1.0 0.6972006
3 5 0.7 150 0.7 0.6750071
3 5 0.7 150 0.8 0.6871424
3 5 0.7 150 0.9 0.6902250
3 5 0.7 150 1.0 0.6916208
3 5 0.8 50 0.7 0.7031942
3 5 0.8 50 0.8 0.7042904
3 5 0.8 50 0.9 0.7029913
3 5 0.8 50 1.0 0.7008410
3 5 0.8 100 0.7 0.6947669
3 5 0.8 100 0.8 0.6956040
3 5 0.8 100 0.9 0.6956920
3 5 0.8 100 1.0 0.6968053
3 5 0.8 150 0.7 0.6752235
3 5 0.8 150 0.8 0.6876239
3 5 0.8 150 0.9 0.6904155
3 5 0.8 150 1.0 0.6917872
3 5 0.9 50 0.7 0.7039123
3 5 0.9 50 0.8 0.7038553
3 5 0.9 50 0.9 0.7034062
3 5 0.9 50 1.0 0.7005793
3 5 0.9 100 0.7 0.6947277
3 5 0.9 100 0.8 0.6958024
3 5 0.9 100 0.9 0.6957621
3 5 0.9 100 1.0 0.6967053
3 5 0.9 150 0.7 0.6754442
3 5 0.9 150 0.8 0.6872657
3 5 0.9 150 0.9 0.6904887
3 5 0.9 150 1.0 0.6917875
3 5 1.0 50 0.7 0.7039520
3 5 1.0 50 0.8 0.7038711
3 5 1.0 50 0.9 0.7035743
3 5 1.0 50 1.0 0.7001945
3 5 1.0 100 0.7 0.6948547
3 5 1.0 100 0.8 0.6960127
3 5 1.0 100 0.9 0.6956369
3 5 1.0 100 1.0 0.6964456
3 5 1.0 150 0.7 0.6752913
3 5 1.0 150 0.8 0.6873894
3 5 1.0 150 0.9 0.6904066
3 5 1.0 150 1.0 0.6917163
3 10 0.1 50 0.7 0.6960146
3 10 0.1 50 0.8 0.6973851
3 10 0.1 50 0.9 0.6980773
3 10 0.1 50 1.0 0.6988181
3 10 0.1 100 0.7 0.6868947
3 10 0.1 100 0.8 0.6887300
3 10 0.1 100 0.9 0.6903831
3 10 0.1 100 1.0 0.6925776
3 10 0.1 150 0.7 0.6678944
3 10 0.1 150 0.8 0.6811713
3 10 0.1 150 0.9 0.6845594
3 10 0.1 150 1.0 0.6869532
3 10 0.2 50 0.7 0.7002571
3 10 0.2 50 0.8 0.7016559
3 10 0.2 50 0.9 0.7017625
3 10 0.2 50 1.0 0.7008473
3 10 0.2 100 0.7 0.6918312
3 10 0.2 100 0.8 0.6928893
3 10 0.2 100 0.9 0.6937602
3 10 0.2 100 1.0 0.6956396
3 10 0.2 150 0.7 0.6718489
3 10 0.2 150 0.8 0.6849642
3 10 0.2 150 0.9 0.6882554
3 10 0.2 150 1.0 0.6895898
3 10 0.3 50 0.7 0.7017087
3 10 0.3 50 0.8 0.7026022
3 10 0.3 50 0.9 0.7018404
3 10 0.3 50 1.0 0.7009190
3 10 0.3 100 0.7 0.6927539
3 10 0.3 100 0.8 0.6939763
3 10 0.3 100 0.9 0.6943230
3 10 0.3 100 1.0 0.6963422
3 10 0.3 150 0.7 0.6729186
3 10 0.3 150 0.8 0.6863580
3 10 0.3 150 0.9 0.6887867
3 10 0.3 150 1.0 0.6903407
3 10 0.4 50 0.7 0.7024487
3 10 0.4 50 0.8 0.7024911
3 10 0.4 50 0.9 0.7023807
3 10 0.4 50 1.0 0.7006401
3 10 0.4 100 0.7 0.6937143
3 10 0.4 100 0.8 0.6947710
3 10 0.4 100 0.9 0.6949678
3 10 0.4 100 1.0 0.6969800
3 10 0.4 150 0.7 0.6737925
3 10 0.4 150 0.8 0.6865619
3 10 0.4 150 0.9 0.6893866
3 10 0.4 150 1.0 0.6907562
3 10 0.5 50 0.7 0.7027287
3 10 0.5 50 0.8 0.7030319
3 10 0.5 50 0.9 0.7020072
3 10 0.5 50 1.0 0.7008428
3 10 0.5 100 0.7 0.6942561
3 10 0.5 100 0.8 0.6945625
3 10 0.5 100 0.9 0.6951076
3 10 0.5 100 1.0 0.6970915
3 10 0.5 150 0.7 0.6740296
3 10 0.5 150 0.8 0.6866038
3 10 0.5 150 0.9 0.6896327
3 10 0.5 150 1.0 0.6909560
3 10 0.6 50 0.7 0.7028483
3 10 0.6 50 0.8 0.7032295
3 10 0.6 50 0.9 0.7025258
3 10 0.6 50 1.0 0.7003593
3 10 0.6 100 0.7 0.6936212
3 10 0.6 100 0.8 0.6950717
3 10 0.6 100 0.9 0.6951578
3 10 0.6 100 1.0 0.6967066
3 10 0.6 150 0.7 0.6743220
3 10 0.6 150 0.8 0.6865581
3 10 0.6 150 0.9 0.6897316
3 10 0.6 150 1.0 0.6907676
3 10 0.7 50 0.7 0.7031198
3 10 0.7 50 0.8 0.7034448
3 10 0.7 50 0.9 0.7027674
3 10 0.7 50 1.0 0.7005848
3 10 0.7 100 0.7 0.6938177
3 10 0.7 100 0.8 0.6954275
3 10 0.7 100 0.9 0.6954119
3 10 0.7 100 1.0 0.6968202
3 10 0.7 150 0.7 0.6744475
3 10 0.7 150 0.8 0.6870209
3 10 0.7 150 0.9 0.6896377
3 10 0.7 150 1.0 0.6907722
3 10 0.8 50 0.7 0.7031203
3 10 0.8 50 0.8 0.7034838
3 10 0.8 50 0.9 0.7029775
3 10 0.8 50 1.0 0.7004393
3 10 0.8 100 0.7 0.6941375
3 10 0.8 100 0.8 0.6953167
3 10 0.8 100 0.9 0.6949855
3 10 0.8 100 1.0 0.6964171
3 10 0.8 150 0.7 0.6748372
3 10 0.8 150 0.8 0.6869942
3 10 0.8 150 0.9 0.6899581
3 10 0.8 150 1.0 0.6908555
3 10 0.9 50 0.7 0.7028958
3 10 0.9 50 0.8 0.7033260
3 10 0.9 50 0.9 0.7028753
3 10 0.9 50 1.0 0.6999273
3 10 0.9 100 0.7 0.6942353
3 10 0.9 100 0.8 0.6949521
3 10 0.9 100 0.9 0.6954755
3 10 0.9 100 1.0 0.6962209
3 10 0.9 150 0.7 0.6751006
3 10 0.9 150 0.8 0.6871578
3 10 0.9 150 0.9 0.6903365
3 10 0.9 150 1.0 0.6901207
3 10 1.0 50 0.7 0.7038150
3 10 1.0 50 0.8 0.7034266
3 10 1.0 50 0.9 0.7026053
3 10 1.0 50 1.0 0.6997525
3 10 1.0 100 0.7 0.6949304
3 10 1.0 100 0.8 0.6956628
3 10 1.0 100 0.9 0.6953694
3 10 1.0 100 1.0 0.6953136
3 10 1.0 150 0.7 0.6750995
3 10 1.0 150 0.8 0.6873089
3 10 1.0 150 0.9 0.6902902
3 10 1.0 150 1.0 0.6893778
4 0 0.1 50 0.7 0.7009290
4 0 0.1 50 0.8 0.7021452
4 0 0.1 50 0.9 0.7025225
4 0 0.1 50 1.0 0.7029128
4 0 0.1 100 0.7 0.6906443
4 0 0.1 100 0.8 0.6930742
4 0 0.1 100 0.9 0.6945475
4 0 0.1 100 1.0 0.6970470
4 0 0.1 150 0.7 0.6710128
4 0 0.1 150 0.8 0.6839025
4 0 0.1 150 0.9 0.6877390
4 0 0.1 150 1.0 0.6903001
4 0 0.2 50 0.7 0.7040865
4 0 0.2 50 0.8 0.7056246
4 0 0.2 50 0.9 0.7052440
4 0 0.2 50 1.0 0.7054072
4 0 0.2 100 0.7 0.6945230
4 0 0.2 100 0.8 0.6962828
4 0 0.2 100 0.9 0.6965258
4 0 0.2 100 1.0 0.6987789
4 0 0.2 150 0.7 0.6738545
4 0 0.2 150 0.8 0.6868601
4 0 0.2 150 0.9 0.6906324
4 0 0.2 150 1.0 0.6927503
4 0 0.3 50 0.7 0.7051077
4 0 0.3 50 0.8 0.7056978
4 0 0.3 50 0.9 0.7056054
4 0 0.3 50 1.0 0.7049805
4 0 0.3 100 0.7 0.6949789
4 0 0.3 100 0.8 0.6965396
4 0 0.3 100 0.9 0.6971307
4 0 0.3 100 1.0 0.6992404
4 0 0.3 150 0.7 0.6749121
4 0 0.3 150 0.8 0.6875272
4 0 0.3 150 0.9 0.6912504
4 0 0.3 150 1.0 0.6931352
4 0 0.4 50 0.7 0.7053166
4 0 0.4 50 0.8 0.7057845
4 0 0.4 50 0.9 0.7056752
4 0 0.4 50 1.0 0.7046527
4 0 0.4 100 0.7 0.6957483
4 0 0.4 100 0.8 0.6973672
4 0 0.4 100 0.9 0.6973438
4 0 0.4 100 1.0 0.6993402
4 0 0.4 150 0.7 0.6749593
4 0 0.4 150 0.8 0.6881598
4 0 0.4 150 0.9 0.6913281
4 0 0.4 150 1.0 0.6933957
4 0 0.5 50 0.7 0.7049416
4 0 0.5 50 0.8 0.7055008
4 0 0.5 50 0.9 0.7051302
4 0 0.5 50 1.0 0.7040718
4 0 0.5 100 0.7 0.6957304
4 0 0.5 100 0.8 0.6967847
4 0 0.5 100 0.9 0.6972300
4 0 0.5 100 1.0 0.6991709
4 0 0.5 150 0.7 0.6751376
4 0 0.5 150 0.8 0.6880945
4 0 0.5 150 0.9 0.6914351
4 0 0.5 150 1.0 0.6934769
4 0 0.6 50 0.7 0.7052117
4 0 0.6 50 0.8 0.7053725
4 0 0.6 50 0.9 0.7052721
4 0 0.6 50 1.0 0.7037243
4 0 0.6 100 0.7 0.6954647
4 0 0.6 100 0.8 0.6968271
4 0 0.6 100 0.9 0.6972709
4 0 0.6 100 1.0 0.6987547
4 0 0.6 150 0.7 0.6754135
4 0 0.6 150 0.8 0.6883721
4 0 0.6 150 0.9 0.6918101
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4 5 0.6 100 0.8 0.6969944
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4 5 0.7 100 0.9 0.6971973
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4 5 0.8 100 0.8 0.6969531
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4 5 1.0 100 0.8 0.6967112
4 5 1.0 100 0.9 0.6966653
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4 10 0.1 50 0.8 0.6991510
4 10 0.1 50 0.9 0.7001808
4 10 0.1 50 1.0 0.7014332
4 10 0.1 100 0.7 0.6873422
4 10 0.1 100 0.8 0.6898269
4 10 0.1 100 0.9 0.6914941
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4 10 0.1 150 0.7 0.6688392
4 10 0.1 150 0.8 0.6813811
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4 10 0.2 100 0.7 0.6931134
4 10 0.2 100 0.8 0.6938063
4 10 0.2 100 0.9 0.6951630
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4 10 0.2 150 0.7 0.6723317
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4 10 0.4 100 0.8 0.6959933
4 10 0.4 100 0.9 0.6966369
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4 10 0.6 50 0.7 0.7047410
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4 10 0.6 100 0.8 0.6961992
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4 10 0.7 50 0.7 0.7049522
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4 10 0.8 50 0.8 0.7049226
4 10 0.8 50 0.9 0.7046596
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4 10 0.8 100 0.7 0.6952917
4 10 0.8 100 0.8 0.6961926
4 10 0.8 100 0.9 0.6965460
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4 10 0.8 150 0.8 0.6877736
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4 10 0.9 50 0.7 0.7049040
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4 10 0.9 50 0.9 0.7047978
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4 10 0.9 100 0.7 0.6953042
4 10 0.9 100 0.8 0.6963709
4 10 0.9 100 0.9 0.6969081
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4 10 0.9 150 0.7 0.6751234
4 10 0.9 150 0.8 0.6874006
4 10 0.9 150 0.9 0.6910329
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4 10 1.0 50 0.7 0.7053491
4 10 1.0 50 0.8 0.7055594
4 10 1.0 50 0.9 0.7051345
4 10 1.0 50 1.0 0.7005992
4 10 1.0 100 0.7 0.6949908
4 10 1.0 100 0.8 0.6965355
4 10 1.0 100 0.9 0.6968668
4 10 1.0 100 1.0 0.6952062
4 10 1.0 150 0.7 0.6752573
4 10 1.0 150 0.8 0.6878852
4 10 1.0 150 0.9 0.6913777
4 10 1.0 150 1.0 0.6898430
5 0 0.1 50 0.7 0.7012300
5 0 0.1 50 0.8 0.7024641
5 0 0.1 50 0.9 0.7031417
5 0 0.1 50 1.0 0.7038022
5 0 0.1 100 0.7 0.6901178
5 0 0.1 100 0.8 0.6930259
5 0 0.1 100 0.9 0.6947087
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5 0 0.3 150 0.9 0.6910984
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5 0 0.4 50 0.8 0.7061874
5 0 0.4 50 0.9 0.7065709
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5 0 0.4 100 0.7 0.6955490
5 0 0.4 100 0.8 0.6973362
5 0 0.4 100 0.9 0.6978049
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5 0 0.4 150 0.7 0.6747909
5 0 0.4 150 0.8 0.6880240
5 0 0.4 150 0.9 0.6915109
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5 0 0.5 50 0.7 0.7058384
5 0 0.5 50 0.8 0.7066048
5 0 0.5 50 0.9 0.7060421
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5 0 0.5 100 0.7 0.6957377
5 0 0.5 100 0.8 0.6971249
5 0 0.5 100 0.9 0.6978984
5 0 0.5 100 1.0 0.7000979
5 0 0.5 150 0.7 0.6754400
5 0 0.5 150 0.8 0.6878225
5 0 0.5 150 0.9 0.6914294
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5 0 0.6 50 0.9 0.7060992
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5 0 0.6 100 0.7 0.6955673
5 0 0.6 100 0.8 0.6973069
5 0 0.6 100 0.9 0.6977000
5 0 0.6 100 1.0 0.6996552
5 0 0.6 150 0.7 0.6754086
5 0 0.6 150 0.8 0.6880499
5 0 0.6 150 0.9 0.6917219
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5 0 0.7 50 0.7 0.7056324
5 0 0.7 50 0.8 0.7062901
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5 0 0.7 100 0.8 0.6972851
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5 0 0.7 150 0.8 0.6881211
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5 0 0.8 100 0.8 0.6972897
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5 0 0.9 100 0.8 0.6969239
5 0 0.9 100 0.9 0.6974535
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5 0 1.0 100 0.9 0.6970150
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5 5 0.7 50 0.7 0.7057355
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5 5 0.8 100 0.8 0.6970948
5 5 0.8 100 0.9 0.6974555
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5 5 0.8 150 0.7 0.6753869
5 5 0.8 150 0.8 0.6882207
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5 5 0.9 50 0.7 0.7057549
5 5 0.9 50 0.8 0.7059098
5 5 0.9 50 0.9 0.7057596
5 5 0.9 50 1.0 0.7035289
5 5 0.9 100 0.7 0.6958878
5 5 0.9 100 0.8 0.6968476
5 5 0.9 100 0.9 0.6975541
5 5 0.9 100 1.0 0.6990467
5 5 0.9 150 0.7 0.6758009
5 5 0.9 150 0.8 0.6881007
5 5 0.9 150 0.9 0.6912807
5 5 0.9 150 1.0 0.6931093
5 5 1.0 50 0.7 0.7052950
5 5 1.0 50 0.8 0.7057469
5 5 1.0 50 0.9 0.7054376
5 5 1.0 50 1.0 0.7024749
5 5 1.0 100 0.7 0.6959286
5 5 1.0 100 0.8 0.6971338
5 5 1.0 100 0.9 0.6975564
5 5 1.0 100 1.0 0.6988183
5 5 1.0 150 0.7 0.6756393
5 5 1.0 150 0.8 0.6882989
5 5 1.0 150 0.9 0.6916324
5 5 1.0 150 1.0 0.6924274
5 10 0.1 50 0.7 0.6987637
5 10 0.1 50 0.8 0.7004394
5 10 0.1 50 0.9 0.7006211
5 10 0.1 50 1.0 0.7025847
5 10 0.1 100 0.7 0.6884223
5 10 0.1 100 0.8 0.6905385
5 10 0.1 100 0.9 0.6919760
5 10 0.1 100 1.0 0.6947938
5 10 0.1 150 0.7 0.6679334
5 10 0.1 150 0.8 0.6813354
5 10 0.1 150 0.9 0.6849331
5 10 0.1 150 1.0 0.6880790
5 10 0.2 50 0.7 0.7036440
5 10 0.2 50 0.8 0.7042164
5 10 0.2 50 0.9 0.7052560
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5 10 0.2 100 0.7 0.6925330
5 10 0.2 100 0.8 0.6943116
5 10 0.2 100 0.9 0.6955547
5 10 0.2 100 1.0 0.6974922
5 10 0.2 150 0.7 0.6718549
5 10 0.2 150 0.8 0.6851107
5 10 0.2 150 0.9 0.6889163
5 10 0.2 150 1.0 0.6909189
5 10 0.3 50 0.7 0.7044656
5 10 0.3 50 0.8 0.7054210
5 10 0.3 50 0.9 0.7053398
5 10 0.3 50 1.0 0.7054393
5 10 0.3 100 0.7 0.6939929
5 10 0.3 100 0.8 0.6957856
5 10 0.3 100 0.9 0.6968202
5 10 0.3 100 1.0 0.6987413
5 10 0.3 150 0.7 0.6731214
5 10 0.3 150 0.8 0.6865086
5 10 0.3 150 0.9 0.6899072
5 10 0.3 150 1.0 0.6919797
5 10 0.4 50 0.7 0.7048453
5 10 0.4 50 0.8 0.7060387
5 10 0.4 50 0.9 0.7058969
5 10 0.4 50 1.0 0.7046139
5 10 0.4 100 0.7 0.6944803
5 10 0.4 100 0.8 0.6963466
5 10 0.4 100 0.9 0.6969428
5 10 0.4 100 1.0 0.6989443
5 10 0.4 150 0.7 0.6734778
5 10 0.4 150 0.8 0.6872464
5 10 0.4 150 0.9 0.6905001
5 10 0.4 150 1.0 0.6925682
5 10 0.5 50 0.7 0.7049218
5 10 0.5 50 0.8 0.7062099
5 10 0.5 50 0.9 0.7058434
5 10 0.5 50 1.0 0.7044165
5 10 0.5 100 0.7 0.6943972
5 10 0.5 100 0.8 0.6963440
5 10 0.5 100 0.9 0.6973264
5 10 0.5 100 1.0 0.6992968
5 10 0.5 150 0.7 0.6742867
5 10 0.5 150 0.8 0.6874030
5 10 0.5 150 0.9 0.6907344
5 10 0.5 150 1.0 0.6927011
5 10 0.6 50 0.7 0.7054171
5 10 0.6 50 0.8 0.7060794
5 10 0.6 50 0.9 0.7058678
5 10 0.6 50 1.0 0.7040635
5 10 0.6 100 0.7 0.6955779
5 10 0.6 100 0.8 0.6962846
5 10 0.6 100 0.9 0.6971978
5 10 0.6 100 1.0 0.6990446
5 10 0.6 150 0.7 0.6743383
5 10 0.6 150 0.8 0.6872299
5 10 0.6 150 0.9 0.6912079
5 10 0.6 150 1.0 0.6929604
5 10 0.7 50 0.7 0.7053846
5 10 0.7 50 0.8 0.7063865
5 10 0.7 50 0.9 0.7056855
5 10 0.7 50 1.0 0.7038929
5 10 0.7 100 0.7 0.6951958
5 10 0.7 100 0.8 0.6966917
5 10 0.7 100 0.9 0.6968960
5 10 0.7 100 1.0 0.6986657
5 10 0.7 150 0.7 0.6747912
5 10 0.7 150 0.8 0.6875482
5 10 0.7 150 0.9 0.6910255
5 10 0.7 150 1.0 0.6926069
5 10 0.8 50 0.7 0.7055788
5 10 0.8 50 0.8 0.7061397
5 10 0.8 50 0.9 0.7059634
5 10 0.8 50 1.0 0.7034881
5 10 0.8 100 0.7 0.6955249
5 10 0.8 100 0.8 0.6966088
5 10 0.8 100 0.9 0.6972111
5 10 0.8 100 1.0 0.6984958
5 10 0.8 150 0.7 0.6748881
5 10 0.8 150 0.8 0.6875713
5 10 0.8 150 0.9 0.6911100
5 10 0.8 150 1.0 0.6922550
5 10 0.9 50 0.7 0.7055165
5 10 0.9 50 0.8 0.7060226
5 10 0.9 50 0.9 0.7058717
5 10 0.9 50 1.0 0.7031936
5 10 0.9 100 0.7 0.6953754
5 10 0.9 100 0.8 0.6964716
5 10 0.9 100 0.9 0.6969594
5 10 0.9 100 1.0 0.6979140
5 10 0.9 150 0.7 0.6751775
5 10 0.9 150 0.8 0.6876694
5 10 0.9 150 0.9 0.6912580
5 10 0.9 150 1.0 0.6915436
5 10 1.0 50 0.7 0.7055618
5 10 1.0 50 0.8 0.7058255
5 10 1.0 50 0.9 0.7061197
5 10 1.0 50 1.0 0.7012082
5 10 1.0 100 0.7 0.6955096
5 10 1.0 100 0.8 0.6965698
5 10 1.0 100 0.9 0.6970429
5 10 1.0 100 1.0 0.6956523
5 10 1.0 150 0.7 0.6748684
5 10 1.0 150 0.8 0.6875177
5 10 1.0 150 0.9 0.6910096
5 10 1.0 150 1.0 0.6894992
6 0 0.1 50 0.7 0.7006737
6 0 0.1 50 0.8 0.7028568
6 0 0.1 50 0.9 0.7032280
6 0 0.1 50 1.0 0.7046407
6 0 0.1 100 0.7 0.6913728
6 0 0.1 100 0.8 0.6929076
6 0 0.1 100 0.9 0.6942893
6 0 0.1 100 1.0 0.6968360
6 0 0.1 150 0.7 0.6708790
6 0 0.1 150 0.8 0.6835067
6 0 0.1 150 0.9 0.6877351
6 0 0.1 150 1.0 0.6896526
6 0 0.2 50 0.7 0.7047040
6 0 0.2 50 0.8 0.7062378
6 0 0.2 50 0.9 0.7066234
6 0 0.2 50 1.0 0.7070489
6 0 0.2 100 0.7 0.6944703
6 0 0.2 100 0.8 0.6965167
6 0 0.2 100 0.9 0.6971609
6 0 0.2 100 1.0 0.6992695
6 0 0.2 150 0.7 0.6735327
6 0 0.2 150 0.8 0.6871385
6 0 0.2 150 0.9 0.6907483
6 0 0.2 150 1.0 0.6928760
6 0 0.3 50 0.7 0.7056853
6 0 0.3 50 0.8 0.7063353
6 0 0.3 50 0.9 0.7066411
6 0 0.3 50 1.0 0.7067395
6 0 0.3 100 0.7 0.6952817
6 0 0.3 100 0.8 0.6972845
6 0 0.3 100 0.9 0.6976459
6 0 0.3 100 1.0 0.6999323
6 0 0.3 150 0.7 0.6745020
6 0 0.3 150 0.8 0.6879254
6 0 0.3 150 0.9 0.6910949
6 0 0.3 150 1.0 0.6934010
6 0 0.4 50 0.7 0.7057144
6 0 0.4 50 0.8 0.7061758
6 0 0.4 50 0.9 0.7062744
6 0 0.4 50 1.0 0.7063077
6 0 0.4 100 0.7 0.6957108
6 0 0.4 100 0.8 0.6971884
6 0 0.4 100 0.9 0.6975398
6 0 0.4 100 1.0 0.6999402
6 0 0.4 150 0.7 0.6753808
6 0 0.4 150 0.8 0.6877385
6 0 0.4 150 0.9 0.6915813
6 0 0.4 150 1.0 0.6937011
6 0 0.5 50 0.7 0.7053442
6 0 0.5 50 0.8 0.7065034
6 0 0.5 50 0.9 0.7064595
6 0 0.5 50 1.0 0.7059119
6 0 0.5 100 0.7 0.6959995
6 0 0.5 100 0.8 0.6971784
6 0 0.5 100 0.9 0.6977134
6 0 0.5 100 1.0 0.6998358
6 0 0.5 150 0.7 0.6751965
6 0 0.5 150 0.8 0.6880253
6 0 0.5 150 0.9 0.6916812
6 0 0.5 150 1.0 0.6936886
6 0 0.6 50 0.7 0.7055215
6 0 0.6 50 0.8 0.7060369
6 0 0.6 50 0.9 0.7062398
6 0 0.6 50 1.0 0.7052299
6 0 0.6 100 0.7 0.6961075
6 0 0.6 100 0.8 0.6969296
6 0 0.6 100 0.9 0.6978433
6 0 0.6 100 1.0 0.6993716
6 0 0.6 150 0.7 0.6752360
6 0 0.6 150 0.8 0.6878948
6 0 0.6 150 0.9 0.6917108
6 0 0.6 150 1.0 0.6937070
6 0 0.7 50 0.7 0.7051831
6 0 0.7 50 0.8 0.7057975
6 0 0.7 50 0.9 0.7059602
6 0 0.7 50 1.0 0.7046987
6 0 0.7 100 0.7 0.6960214
6 0 0.7 100 0.8 0.6970960
6 0 0.7 100 0.9 0.6975522
6 0 0.7 100 1.0 0.6994878
6 0 0.7 150 0.7 0.6752051
6 0 0.7 150 0.8 0.6881157
6 0 0.7 150 0.9 0.6918658
6 0 0.7 150 1.0 0.6934325
6 0 0.8 50 0.7 0.7051623
6 0 0.8 50 0.8 0.7060857
6 0 0.8 50 0.9 0.7058986
6 0 0.8 50 1.0 0.7037701
6 0 0.8 100 0.7 0.6962335
6 0 0.8 100 0.8 0.6969912
6 0 0.8 100 0.9 0.6971486
6 0 0.8 100 1.0 0.6991992
6 0 0.8 150 0.7 0.6758173
6 0 0.8 150 0.8 0.6881854
6 0 0.8 150 0.9 0.6915954
6 0 0.8 150 1.0 0.6929497
6 0 0.9 50 0.7 0.7049903
6 0 0.9 50 0.8 0.7056040
6 0 0.9 50 0.9 0.7059171
6 0 0.9 50 1.0 0.7035765
6 0 0.9 100 0.7 0.6960738
6 0 0.9 100 0.8 0.6966422
6 0 0.9 100 0.9 0.6972099
6 0 0.9 100 1.0 0.6988984
6 0 0.9 150 0.7 0.6756419
6 0 0.9 150 0.8 0.6886390
6 0 0.9 150 0.9 0.6914297
6 0 0.9 150 1.0 0.6926338
6 0 1.0 50 0.7 0.7050054
6 0 1.0 50 0.8 0.7059781
6 0 1.0 50 0.9 0.7054466
6 0 1.0 50 1.0 0.7032312
6 0 1.0 100 0.7 0.6958262
6 0 1.0 100 0.8 0.6967292
6 0 1.0 100 0.9 0.6976677
6 0 1.0 100 1.0 0.6985792
6 0 1.0 150 0.7 0.6759829
6 0 1.0 150 0.8 0.6880865
6 0 1.0 150 0.9 0.6915472
6 0 1.0 150 1.0 0.6923658
6 5 0.1 50 0.7 0.7004264
6 5 0.1 50 0.8 0.7025021
6 5 0.1 50 0.9 0.7029699
6 5 0.1 50 1.0 0.7043391
6 5 0.1 100 0.7 0.6905961
6 5 0.1 100 0.8 0.6931436
6 5 0.1 100 0.9 0.6940269
6 5 0.1 100 1.0 0.6967177
6 5 0.1 150 0.7 0.6702510
6 5 0.1 150 0.8 0.6833203
6 5 0.1 150 0.9 0.6876241
6 5 0.1 150 1.0 0.6895592
6 5 0.2 50 0.7 0.7051060
6 5 0.2 50 0.8 0.7055117
6 5 0.2 50 0.9 0.7064861
6 5 0.2 50 1.0 0.7064669
6 5 0.2 100 0.7 0.6940722
6 5 0.2 100 0.8 0.6957044
6 5 0.2 100 0.9 0.6973473
6 5 0.2 100 1.0 0.6993729
6 5 0.2 150 0.7 0.6740490
6 5 0.2 150 0.8 0.6867069
6 5 0.2 150 0.9 0.6907353
6 5 0.2 150 1.0 0.6925312
6 5 0.3 50 0.7 0.7058334
6 5 0.3 50 0.8 0.7063838
6 5 0.3 50 0.9 0.7065057
6 5 0.3 50 1.0 0.7065287
6 5 0.3 100 0.7 0.6949430
6 5 0.3 100 0.8 0.6972537
6 5 0.3 100 0.9 0.6976132
6 5 0.3 100 1.0 0.7001636
6 5 0.3 150 0.7 0.6744077
6 5 0.3 150 0.8 0.6876208
6 5 0.3 150 0.9 0.6911642
6 5 0.3 150 1.0 0.6934419
6 5 0.4 50 0.7 0.7054300
6 5 0.4 50 0.8 0.7067104
6 5 0.4 50 0.9 0.7064997
6 5 0.4 50 1.0 0.7058686
6 5 0.4 100 0.7 0.6957774
6 5 0.4 100 0.8 0.6971831
6 5 0.4 100 0.9 0.6979543
6 5 0.4 100 1.0 0.7005779
6 5 0.4 150 0.7 0.6751461
6 5 0.4 150 0.8 0.6876672
6 5 0.4 150 0.9 0.6919094
6 5 0.4 150 1.0 0.6935059
6 5 0.5 50 0.7 0.7052655
6 5 0.5 50 0.8 0.7066079
6 5 0.5 50 0.9 0.7067037
6 5 0.5 50 1.0 0.7057422
6 5 0.5 100 0.7 0.6958067
6 5 0.5 100 0.8 0.6972755
6 5 0.5 100 0.9 0.6978592
6 5 0.5 100 1.0 0.7001701
6 5 0.5 150 0.7 0.6750763
6 5 0.5 150 0.8 0.6876218
6 5 0.5 150 0.9 0.6918488
6 5 0.5 150 1.0 0.6935274
6 5 0.6 50 0.7 0.7053043
6 5 0.6 50 0.8 0.7060047
6 5 0.6 50 0.9 0.7057572
6 5 0.6 50 1.0 0.7048934
6 5 0.6 100 0.7 0.6957143
6 5 0.6 100 0.8 0.6968116
6 5 0.6 100 0.9 0.6977523
6 5 0.6 100 1.0 0.7001940
6 5 0.6 150 0.7 0.6749861
6 5 0.6 150 0.8 0.6880414
6 5 0.6 150 0.9 0.6918575
6 5 0.6 150 1.0 0.6935177
6 5 0.7 50 0.7 0.7053448
6 5 0.7 50 0.8 0.7064198
6 5 0.7 50 0.9 0.7060378
6 5 0.7 50 1.0 0.7047872
6 5 0.7 100 0.7 0.6957265
6 5 0.7 100 0.8 0.6967480
6 5 0.7 100 0.9 0.6978300
6 5 0.7 100 1.0 0.6999579
6 5 0.7 150 0.7 0.6753646
6 5 0.7 150 0.8 0.6879822
6 5 0.7 150 0.9 0.6914436
6 5 0.7 150 1.0 0.6933279
6 5 0.8 50 0.7 0.7050701
6 5 0.8 50 0.8 0.7059783
6 5 0.8 50 0.9 0.7059600
6 5 0.8 50 1.0 0.7045495
6 5 0.8 100 0.7 0.6962599
6 5 0.8 100 0.8 0.6970115
6 5 0.8 100 0.9 0.6975616
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6 5 0.8 150 0.8 0.6880702
6 5 0.8 150 0.9 0.6917219
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6 5 0.9 50 0.7 0.7046879
6 5 0.9 50 0.8 0.7055988
6 5 0.9 50 0.9 0.7055416
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6 5 0.9 100 0.7 0.6955015
6 5 0.9 100 0.8 0.6969526
6 5 0.9 100 0.9 0.6975881
6 5 0.9 100 1.0 0.6993016
6 5 0.9 150 0.7 0.6757126
6 5 0.9 150 0.8 0.6881770
6 5 0.9 150 0.9 0.6917162
6 5 0.9 150 1.0 0.6928738
6 5 1.0 50 0.7 0.7048238
6 5 1.0 50 0.8 0.7055742
6 5 1.0 50 0.9 0.7054184
6 5 1.0 50 1.0 0.7028669
6 5 1.0 100 0.7 0.6961463
6 5 1.0 100 0.8 0.6966833
6 5 1.0 100 0.9 0.6976436
6 5 1.0 100 1.0 0.6989569
6 5 1.0 150 0.7 0.6757301
6 5 1.0 150 0.8 0.6884142
6 5 1.0 150 0.9 0.6915386
6 5 1.0 150 1.0 0.6922994
6 10 0.1 50 0.7 0.6985578
6 10 0.1 50 0.8 0.7005921
6 10 0.1 50 0.9 0.7019566
6 10 0.1 50 1.0 0.7030270
6 10 0.1 100 0.7 0.6874980
6 10 0.1 100 0.8 0.6906198
6 10 0.1 100 0.9 0.6917988
6 10 0.1 100 1.0 0.6948796
6 10 0.1 150 0.7 0.6677611
6 10 0.1 150 0.8 0.6809801
6 10 0.1 150 0.9 0.6858153
6 10 0.1 150 1.0 0.6878466
6 10 0.2 50 0.7 0.7041755
6 10 0.2 50 0.8 0.7053791
6 10 0.2 50 0.9 0.7054754
6 10 0.2 50 1.0 0.7059431
6 10 0.2 100 0.7 0.6925856
6 10 0.2 100 0.8 0.6944433
6 10 0.2 100 0.9 0.6955449
6 10 0.2 100 1.0 0.6981810
6 10 0.2 150 0.7 0.6718760
6 10 0.2 150 0.8 0.6850326
6 10 0.2 150 0.9 0.6890994
6 10 0.2 150 1.0 0.6903606
6 10 0.3 50 0.7 0.7049802
6 10 0.3 50 0.8 0.7058742
6 10 0.3 50 0.9 0.7059704
6 10 0.3 50 1.0 0.7060569
6 10 0.3 100 0.7 0.6941620
6 10 0.3 100 0.8 0.6954333
6 10 0.3 100 0.9 0.6968070
6 10 0.3 100 1.0 0.6987432
6 10 0.3 150 0.7 0.6727965
6 10 0.3 150 0.8 0.6864605
6 10 0.3 150 0.9 0.6899111
6 10 0.3 150 1.0 0.6919904
6 10 0.4 50 0.7 0.7052123
6 10 0.4 50 0.8 0.7052841
6 10 0.4 50 0.9 0.7062092
6 10 0.4 50 1.0 0.7053606
6 10 0.4 100 0.7 0.6949183
6 10 0.4 100 0.8 0.6965265
6 10 0.4 100 0.9 0.6969523
6 10 0.4 100 1.0 0.6994779
6 10 0.4 150 0.7 0.6739230
6 10 0.4 150 0.8 0.6868020
6 10 0.4 150 0.9 0.6906816
6 10 0.4 150 1.0 0.6923422
6 10 0.5 50 0.7 0.7056456
6 10 0.5 50 0.8 0.7061590
6 10 0.5 50 0.9 0.7063185
6 10 0.5 50 1.0 0.7050119
6 10 0.5 100 0.7 0.6949381
6 10 0.5 100 0.8 0.6964511
6 10 0.5 100 0.9 0.6969027
6 10 0.5 100 1.0 0.6994147
6 10 0.5 150 0.7 0.6739791
6 10 0.5 150 0.8 0.6872910
6 10 0.5 150 0.9 0.6906958
6 10 0.5 150 1.0 0.6927990
6 10 0.6 50 0.7 0.7052055
6 10 0.6 50 0.8 0.7061031
6 10 0.6 50 0.9 0.7062667
6 10 0.6 50 1.0 0.7048546
6 10 0.6 100 0.7 0.6954176
6 10 0.6 100 0.8 0.6966580
6 10 0.6 100 0.9 0.6972124
6 10 0.6 100 1.0 0.6990547
6 10 0.6 150 0.7 0.6743712
6 10 0.6 150 0.8 0.6874653
6 10 0.6 150 0.9 0.6907944
6 10 0.6 150 1.0 0.6925736
6 10 0.7 50 0.7 0.7056350
6 10 0.7 50 0.8 0.7062630
6 10 0.7 50 0.9 0.7059341
6 10 0.7 50 1.0 0.7045113
6 10 0.7 100 0.7 0.6951189
6 10 0.7 100 0.8 0.6964660
6 10 0.7 100 0.9 0.6970890
6 10 0.7 100 1.0 0.6990259
6 10 0.7 150 0.7 0.6745933
6 10 0.7 150 0.8 0.6874142
6 10 0.7 150 0.9 0.6913136
6 10 0.7 150 1.0 0.6924787
6 10 0.8 50 0.7 0.7052782
6 10 0.8 50 0.8 0.7061482
6 10 0.8 50 0.9 0.7061082
6 10 0.8 50 1.0 0.7041775
6 10 0.8 100 0.7 0.6950781
6 10 0.8 100 0.8 0.6966413
6 10 0.8 100 0.9 0.6969835
6 10 0.8 100 1.0 0.6986563
6 10 0.8 150 0.7 0.6749783
6 10 0.8 150 0.8 0.6878747
6 10 0.8 150 0.9 0.6912996
6 10 0.8 150 1.0 0.6922363
6 10 0.9 50 0.7 0.7050758
6 10 0.9 50 0.8 0.7054718
6 10 0.9 50 0.9 0.7059615
6 10 0.9 50 1.0 0.7036011
6 10 0.9 100 0.7 0.6953913
6 10 0.9 100 0.8 0.6965997
6 10 0.9 100 0.9 0.6973141
6 10 0.9 100 1.0 0.6983669
6 10 0.9 150 0.7 0.6750798
6 10 0.9 150 0.8 0.6877210
6 10 0.9 150 0.9 0.6911367
6 10 0.9 150 1.0 0.6915096
6 10 1.0 50 0.7 0.7052515
6 10 1.0 50 0.8 0.7057473
6 10 1.0 50 0.9 0.7061715
6 10 1.0 50 1.0 0.7019714
6 10 1.0 100 0.7 0.6958726
6 10 1.0 100 0.8 0.6963165
6 10 1.0 100 0.9 0.6971297
6 10 1.0 100 1.0 0.6955505
6 10 1.0 150 0.7 0.6753102
6 10 1.0 150 0.8 0.6876899
6 10 1.0 150 0.9 0.6910715
6 10 1.0 150 1.0 0.6893622
Sens Spec
0.5666337 0.7126202
0.5685149 0.7157209
0.5669307 0.7155659
0.5710891 0.7164651
0.5835644 0.6917209
0.5854455 0.6919070
0.5841584 0.6951008
0.5819802 0.6987907
0.5678218 0.6763721
0.5833663 0.6803101
0.5897030 0.6845271
0.5900000 0.6862016
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0.5972277 0.6805271
0.5953465 0.6821705
0.5940594 0.6848992
0.5898020 0.6555969
0.6013861 0.6677209
0.6031683 0.6706667
0.5998020 0.6728062
0.5774257 0.7070698
0.5751485 0.7093953
0.5725743 0.7097984
0.5721782 0.7116589
0.5984158 0.6802791
0.5985149 0.6800000
0.5959406 0.6826667
0.5952475 0.6842171
0.5914851 0.6546047
0.6035644 0.6669147
0.6012871 0.6708527
0.5988119 0.6721240
0.5780198 0.7075659
0.5748515 0.7082171
0.5718812 0.7120310
0.5712871 0.7110078
0.5977228 0.6792248
0.5956436 0.6805891
0.5951485 0.6813953
0.5951485 0.6837519
0.5933663 0.6521240
0.6048515 0.6658915
0.6032673 0.6702636
0.6005941 0.6714729
0.5759406 0.7073798
0.5739604 0.7086822
0.5713861 0.7118760
0.5705941 0.7122791
0.5945545 0.6791008
0.5958416 0.6801550
0.5946535 0.6813953
0.5923762 0.6826667
0.5930693 0.6536124
0.6060396 0.6650543
0.6035644 0.6699225
0.6025743 0.6709457
0.5743564 0.7068527
0.5741584 0.7090853
0.5730693 0.7112868
0.5712871 0.7108837
0.5967327 0.6786667
0.5981188 0.6788837
0.5954455 0.6817054
0.5912871 0.6840930
0.5940594 0.6544186
0.6058416 0.6656124
0.6033663 0.6704496
0.6023762 0.6700155
0.5729703 0.7067287
0.5721782 0.7085271
0.5697030 0.7126512
0.5714851 0.7075039
0.5978218 0.6781085
0.5954455 0.6795349
0.5947525 0.6810853
0.5907921 0.6823566
0.5964356 0.6525891
0.6037624 0.6649612
0.6019802 0.6690853
0.6000990 0.6693023
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 = 10, colsample_bytree = 0.8, min_child_weight = 150
and subsample = 0.8.
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, 3388, 3388, 3388, 3388, 3388, ...
Resampling results across tuning parameters:
C ROC Sens Spec
2.500000e-01 0.6871801 0.13138614 0.9675659
5.000000e-01 0.6871881 0.13623762 0.9661395
1.000000e+00 0.6867465 0.13287129 0.9680000
2.000000e+00 0.6841850 0.12297030 0.9691163
4.000000e+00 0.6783317 0.09445545 0.9754109
8.000000e+00 0.6660218 0.06653465 0.9820155
1.600000e+01 0.6520748 0.04594059 0.9876279
3.200000e+01 0.6431164 0.03504950 0.9899535
6.400000e+01 0.6363489 0.02919782 0.9909824
1.280000e+02 0.6341330 0.02909679 0.9899383
2.560000e+02 0.6334934 0.02970297 0.9896641
5.120000e+02 0.6343512 0.03030915 0.9894953
1.024000e+03 0.6338953 0.02849060 0.9904762
2.048000e+03 0.6348831 0.03162255 0.9887676
4.096000e+03 0.6343512 0.03000606 0.9894953
8.192000e+03 0.6349351 0.02841584 0.9908217
1.638400e+04 0.6337099 0.02991363 0.9896751
3.276800e+04 0.6348831 0.03030915 0.9901281
6.553600e+04 0.6328699 0.03086160 0.9892462
1.310720e+05 0.6338953 0.03010709 0.9897485
2.621440e+05 0.6349351 0.03029703 0.9900155
5.242880e+05 0.6349351 0.03039604 0.9896744
1.048576e+06 0.6349351 0.02960396 0.9897054
2.097152e+06 0.6332619 0.03135314 0.9893734
4.194304e+06 0.6349351 0.03198020 0.9892403
8.388608e+06 0.6348831 0.02980400 0.9900649
1.677722e+07 0.6324334 0.03117759 0.9896751
3.355443e+07 0.6349351 0.02762376 0.9906047
6.710886e+07 0.6349351 0.02782178 0.9905116
1.342177e+08 0.6350774 0.03073432 0.9903424
2.684355e+08 0.6325425 0.03001896 0.9894112
5.368709e+08 0.6349351 0.02881188 0.9897984
1.073742e+09 0.6349351 0.03019802 0.9900155
2.147484e+09 0.6349675 0.02869267 0.9903813
4.294967e+09 0.6349351 0.02772277 0.9911008
8.589935e+09 0.6342254 0.02898102 0.9898902
1.717987e+10 0.6336786 0.02671205 0.9907946
3.435974e+10 0.6340690 0.03382838 0.9882429
6.871948e+10 0.6349351 0.02841584 0.9905116
1.374390e+11 0.6346022 0.03071328 0.9900965
2.748779e+11 0.6349351 0.02881188 0.9907597
5.497558e+11 0.6349351 0.02940594 0.9902016
1.099512e+12 0.6344577 0.02939988 0.9899699
2.199023e+12 0.6349351 0.02940594 0.9898605
4.398047e+12 0.6340991 0.03012429 0.9900049
8.796093e+12 0.6345174 0.03073432 0.9895349
1.759219e+13 0.6349351 0.02960396 0.9902946
3.518437e+13 0.6338953 0.03051121 0.9897168
7.036874e+13 0.6343512 0.02838957 0.9903180
1.407375e+14 0.6343512 0.02929885 0.9902547
2.814750e+14 0.6349351 0.02970297 0.9901085
5.629500e+14 0.6342858 0.03222873 0.9892422
1.125900e+15 0.6349351 0.03168317 0.9890853
2.251800e+15 0.6345803 0.03081431 0.9896219
4.503600e+15 0.6349351 0.03039604 0.9894884
9.007199e+15 0.6338800 0.02950091 0.9900016
1.801440e+16 0.6342858 0.03101637 0.9894321
3.602880e+16 0.6349351 0.02871287 0.9904496
7.205759e+16 0.6348905 0.02960194 0.9897801
1.441152e+17 0.6345174 0.03176568 0.9895995
2.882304e+17 0.6342858 0.02950091 0.9898434
5.764608e+17 0.6349351 0.03118812 0.9891163
1.152922e+18 0.6344740 0.02859163 0.9907926
2.305843e+18 0.6344787 0.03063119 0.9901486
4.611686e+18 0.6342859 0.03197195 0.9897287
9.223372e+18 0.6349351 0.02960396 0.9899535
1.844674e+19 0.6338887 0.03052805 0.9897610
3.689349e+19 0.6349351 0.02940594 0.9900155
7.378698e+19 0.6349351 0.02930693 0.9905736
1.475740e+20 0.6345780 0.03051121 0.9890524
2.951479e+20 0.6338800 0.03273389 0.9888942
5.902958e+20 0.6343512 0.02869267 0.9901281
1.180592e+21 0.6353917 0.02717721 0.9915520
2.361183e+21 0.6349351 0.03079208 0.9895194
4.722366e+21 0.6349351 0.02940594 0.9894574
9.444733e+21 0.6342859 0.03042492 0.9899871
1.888947e+22 0.6348905 0.03071328 0.9898750
3.777893e+22 0.6346899 0.03042492 0.9899225
7.555786e+22 0.6349351 0.03069307 0.9894574
1.511157e+23 0.6338009 0.03125000 0.9892442
3.022315e+23 0.6351265 0.02869267 0.9906028
6.044629e+23 0.6342858 0.02849060 0.9906977
1.208926e+24 0.6351635 0.02867162 0.9905362
2.417852e+24 0.6344845 0.02970297 0.9901486
4.835703e+24 0.6343540 0.03197195 0.9890504
9.671407e+24 0.6349351 0.02861386 0.9904186
1.934281e+25 0.6340775 0.03042492 0.9896964
3.868563e+25 0.6342858 0.03303698 0.9888942
7.737125e+25 0.6349351 0.02851485 0.9907287
1.547425e+26 0.6348930 0.03094059 0.9899871
3.094850e+26 0.6349351 0.02920792 0.9897674
6.189700e+26 0.6338049 0.03011551 0.9898579
1.237940e+27 0.6349351 0.03089109 0.9897364
2.475880e+27 0.6349351 0.02960396 0.9898915
4.951760e+27 0.6349351 0.03198020 0.9894264
9.903520e+27 0.6342267 0.03063119 0.9900840
1.980704e+28 0.6338049 0.03104373 0.9889858
3.961408e+28 0.6348905 0.03071328 0.9899067
7.922816e+28 0.6327966 0.03176568 0.9889858
1.584563e+29 0.6349351 0.02970297 0.9902636
Tuning parameter 'sigma' was held constant at a value of 0.003556391
ROC was used to select the optimal model using the largest value.
The final values used for the model were sigma = 0.003556391 and C = 0.5.
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: 3388, 3388, 3388, 3388, 3388, 3388, ...
Resampling results across tuning parameters:
alpha lambda ROC Sens Spec
0.1000000 0.0002022569 0.6988450 0.2300000000 0.9330853
0.1000000 0.0002215682 0.6989304 0.2300000000 0.9331473
0.1000000 0.0002427232 0.6989959 0.2300990099 0.9332713
0.1000000 0.0002658982 0.6990804 0.2300990099 0.9333643
0.1000000 0.0002912858 0.6991666 0.2297029703 0.9332713
0.1000000 0.0003190974 0.6992557 0.2292079208 0.9333643
0.1000000 0.0003495644 0.6993798 0.2295049505 0.9334884
0.1000000 0.0003829404 0.6994842 0.2292079208 0.9335504
0.1000000 0.0004195030 0.6996040 0.2291089109 0.9337364
0.1000000 0.0004595567 0.6997355 0.2290099010 0.9338605
0.1000000 0.0005034346 0.6998766 0.2289108911 0.9341395
0.1000000 0.0005515019 0.7000192 0.2288118812 0.9343876
0.1000000 0.0006041586 0.7001798 0.2285148515 0.9344496
0.1000000 0.0006618429 0.7003257 0.2285148515 0.9346667
0.1000000 0.0007250348 0.7005086 0.2285148515 0.9348217
0.1000000 0.0007942603 0.7006676 0.2282178218 0.9349767
0.1000000 0.0008700953 0.7008467 0.2277227723 0.9350078
0.1000000 0.0009531709 0.7010294 0.2276237624 0.9353798
0.1000000 0.0010441784 0.7012217 0.2274257426 0.9357209
0.1000000 0.0011438753 0.7014391 0.2268316832 0.9360620
0.1000000 0.0012530911 0.7016743 0.2261386139 0.9364341
0.1000000 0.0013727347 0.7018671 0.2254455446 0.9368372
0.1000000 0.0015038017 0.7020815 0.2252475248 0.9370853
0.1000000 0.0016473828 0.7023319 0.2253465347 0.9373333
0.1000000 0.0018046729 0.7026004 0.2243564356 0.9375194
0.1000000 0.0019769808 0.7028460 0.2234653465 0.9377984
0.1000000 0.0021657404 0.7031223 0.2235643564 0.9382326
0.1000000 0.0023725226 0.7033935 0.2228712871 0.9387597
0.1000000 0.0025990482 0.7036737 0.2214851485 0.9392248
0.1000000 0.0028472020 0.7039850 0.2206930693 0.9399690
0.1000000 0.0031190494 0.7042881 0.2195049505 0.9404961
0.1000000 0.0034168523 0.7045786 0.2186138614 0.9412713
0.1000000 0.0037430891 0.7048674 0.2171287129 0.9416744
0.1000000 0.0041004746 0.7051654 0.2154455446 0.9426357
0.1000000 0.0044919829 0.7054842 0.2141584158 0.9433798
0.1000000 0.0049208718 0.7057851 0.2130693069 0.9440620
0.1000000 0.0053907105 0.7061002 0.2112871287 0.9447752
0.1000000 0.0059054089 0.7064768 0.2095049505 0.9455504
0.1000000 0.0064692500 0.7067723 0.2072277228 0.9462016
0.1000000 0.0070869259 0.7071100 0.2048514851 0.9471008
0.1000000 0.0077635768 0.7074371 0.2028712871 0.9483101
0.1000000 0.0085048334 0.7077545 0.2000000000 0.9492403
0.1000000 0.0093168644 0.7080889 0.1981188119 0.9503566
0.1000000 0.0102064270 0.7083792 0.1953465347 0.9513488
0.1000000 0.0111809240 0.7086990 0.1926732673 0.9525891
0.1000000 0.0122484647 0.7090287 0.1901980198 0.9537674
0.1000000 0.0134179329 0.7093719 0.1885148515 0.9552248
0.1000000 0.0146990604 0.7096852 0.1851485149 0.9566822
0.1000000 0.0161025084 0.7099753 0.1820792079 0.9582016
0.1000000 0.0176399559 0.7102758 0.1787128713 0.9594109
0.1000000 0.0193241970 0.7105652 0.1770297030 0.9604651
0.1000000 0.0211692473 0.7108495 0.1733663366 0.9618915
0.1000000 0.0231904607 0.7110969 0.1698019802 0.9635039
0.1000000 0.0254046571 0.7113132 0.1663366337 0.9646512
0.1000000 0.0278302622 0.7114472 0.1624752475 0.9659845
0.1000000 0.0304874610 0.7114538 0.1582178218 0.9676899
0.1000000 0.0333983659 0.7113749 0.1539603960 0.9689922
0.1000000 0.0365872004 0.7112090 0.1496039604 0.9704806
0.1000000 0.0400805008 0.7109646 0.1452475248 0.9718760
0.1000000 0.0439073372 0.7106760 0.1388118812 0.9734574
0.1000000 0.0480995552 0.7103583 0.1328712871 0.9748837
0.1000000 0.0526920409 0.7098666 0.1288118812 0.9760620
0.1000000 0.0577230115 0.7092920 0.1239603960 0.9773643
0.1000000 0.0632343329 0.7085598 0.1186138614 0.9789767
0.1000000 0.0692718684 0.7076611 0.1138613861 0.9805891
0.1000000 0.0758858603 0.7065910 0.1075247525 0.9822326
0.1000000 0.0831313479 0.7054230 0.0999009901 0.9840000
0.1000000 0.0910686257 0.7041323 0.0943564356 0.9853023
0.1000000 0.0997637449 0.7025763 0.0893069307 0.9866977
0.1000000 0.1092890633 0.7009396 0.0816831683 0.9880620
0.1000000 0.1197238472 0.6991683 0.0740594059 0.9896744
0.1000000 0.1311549313 0.6973996 0.0661386139 0.9906357
0.1000000 0.1436774411 0.6956473 0.0571287129 0.9917519
0.1000000 0.1573955846 0.6937870 0.0481188119 0.9930853
0.1090909 0.0002022569 0.6988750 0.2298019802 0.9331473
0.1090909 0.0002215682 0.6989557 0.2298019802 0.9332093
0.1090909 0.0002427232 0.6990317 0.2298019802 0.9333333
0.1090909 0.0002658982 0.6991157 0.2298019802 0.9333953
0.1090909 0.0002912858 0.6991883 0.2295049505 0.9334264
0.1090909 0.0003190974 0.6992879 0.2293069307 0.9335504
0.1090909 0.0003495644 0.6994089 0.2293069307 0.9335814
0.1090909 0.0003829404 0.6995152 0.2291089109 0.9336434
0.1090909 0.0004195030 0.6996439 0.2290099010 0.9337984
0.1090909 0.0004595567 0.6997802 0.2288118812 0.9340465
0.1090909 0.0005034346 0.6999203 0.2290099010 0.9342326
0.1090909 0.0005515019 0.7000728 0.2288118812 0.9343876
0.1090909 0.0006041586 0.7002289 0.2282178218 0.9345116
0.1090909 0.0006618429 0.7003956 0.2283168317 0.9347287
0.1090909 0.0007250348 0.7005580 0.2285148515 0.9348527
0.1090909 0.0007942603 0.7007176 0.2279207921 0.9349147
0.1090909 0.0008700953 0.7009127 0.2276237624 0.9351938
0.1090909 0.0009531709 0.7010971 0.2274257426 0.9355039
0.1090909 0.0010441784 0.7012930 0.2267326733 0.9358450
0.1090909 0.0011438753 0.7015157 0.2262376238 0.9362171
0.1090909 0.0012530911 0.7017449 0.2259405941 0.9366202
0.1090909 0.0013727347 0.7019498 0.2251485149 0.9369612
0.1090909 0.0015038017 0.7021764 0.2254455446 0.9371783
0.1090909 0.0016473828 0.7024445 0.2247524752 0.9374264
0.1090909 0.0018046729 0.7027149 0.2238613861 0.9376744
0.1090909 0.0019769808 0.7029651 0.2234653465 0.9380155
0.1090909 0.0021657404 0.7032440 0.2231683168 0.9383566
0.1090909 0.0023725226 0.7035243 0.2222772277 0.9389767
0.1090909 0.0025990482 0.7038222 0.2211881188 0.9395039
0.1090909 0.0028472020 0.7041291 0.2203960396 0.9402171
0.1090909 0.0031190494 0.7044508 0.2194059406 0.9407132
0.1090909 0.0034168523 0.7047026 0.2184158416 0.9415194
0.1090909 0.0037430891 0.7050168 0.2162376238 0.9420775
0.1090909 0.0041004746 0.7053214 0.2145544554 0.9428837
0.1090909 0.0044919829 0.7056282 0.2134653465 0.9437519
0.1090909 0.0049208718 0.7059435 0.2123762376 0.9443411
0.1090909 0.0053907105 0.7062901 0.2102970297 0.9452403
0.1090909 0.0059054089 0.7066472 0.2082178218 0.9457364
0.1090909 0.0064692500 0.7069416 0.2058415842 0.9465736
0.1090909 0.0070869259 0.7072781 0.2043564356 0.9475039
0.1090909 0.0077635768 0.7076167 0.2017821782 0.9486822
0.1090909 0.0085048334 0.7079384 0.1986138614 0.9497674
0.1090909 0.0093168644 0.7082800 0.1972277228 0.9509147
0.1090909 0.0102064270 0.7085824 0.1939603960 0.9520310
0.1090909 0.0111809240 0.7088921 0.1911881188 0.9531163
0.1090909 0.0122484647 0.7092491 0.1887128713 0.9544806
0.1090909 0.0134179329 0.7095868 0.1870297030 0.9557519
0.1090909 0.0146990604 0.7098726 0.1842574257 0.9575814
0.1090909 0.0161025084 0.7101778 0.1808910891 0.9590078
0.1090909 0.0176399559 0.7104709 0.1778217822 0.9599380
0.1090909 0.0193241970 0.7107440 0.1753465347 0.9612713
0.1090909 0.0211692473 0.7110398 0.1718811881 0.9629767
0.1090909 0.0231904607 0.7112900 0.1681188119 0.9642171
0.1090909 0.0254046571 0.7114334 0.1646534653 0.9654264
0.1090909 0.0278302622 0.7114833 0.1603960396 0.9671628
0.1090909 0.0304874610 0.7114442 0.1558415842 0.9684341
0.1090909 0.0333983659 0.7112928 0.1517821782 0.9697054
0.1090909 0.0365872004 0.7110768 0.1470297030 0.9713488
0.1090909 0.0400805008 0.7107968 0.1414851485 0.9729302
0.1090909 0.0439073372 0.7104958 0.1352475248 0.9745426
0.1090909 0.0480995552 0.7100309 0.1304950495 0.9757519
0.1090909 0.0526920409 0.7094531 0.1266336634 0.9768062
0.1090909 0.0577230115 0.7087893 0.1211881188 0.9782636
0.1090909 0.0632343329 0.7078726 0.1159405941 0.9800000
0.1090909 0.0692718684 0.7068504 0.1102970297 0.9817054
0.1090909 0.0758858603 0.7056574 0.1019801980 0.9832868
0.1090909 0.0831313479 0.7044172 0.0962376238 0.9848372
0.1090909 0.0910686257 0.7028031 0.0910891089 0.9861705
0.1090909 0.0997637449 0.7011973 0.0848514851 0.9875349
0.1090909 0.1092890633 0.6994466 0.0763366337 0.9891473
0.1090909 0.1197238472 0.6976143 0.0693069307 0.9901085
0.1090909 0.1311549313 0.6958312 0.0600000000 0.9915349
0.1090909 0.1436774411 0.6939537 0.0506930693 0.9924651
0.1090909 0.1573955846 0.6919482 0.0426732673 0.9941085
0.1181818 0.0002022569 0.6988679 0.2297029703 0.9331163
0.1181818 0.0002215682 0.6989517 0.2299009901 0.9331783
0.1181818 0.0002427232 0.6990343 0.2298019802 0.9332713
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1.0000000 0.0003495644 0.7018523 0.2242574257 0.9374574
1.0000000 0.0003829404 0.7021897 0.2235643564 0.9379225
1.0000000 0.0004195030 0.7025515 0.2234653465 0.9382326
1.0000000 0.0004595567 0.7029579 0.2224752475 0.9384186
1.0000000 0.0005034346 0.7033441 0.2210891089 0.9391318
1.0000000 0.0005515019 0.7037427 0.2198019802 0.9397829
1.0000000 0.0006041586 0.7041299 0.2192079208 0.9401550
1.0000000 0.0006618429 0.7044482 0.2178217822 0.9407442
1.0000000 0.0007250348 0.7048022 0.2173267327 0.9415814
1.0000000 0.0007942603 0.7051418 0.2159405941 0.9422946
1.0000000 0.0008700953 0.7055057 0.2145544554 0.9429457
1.0000000 0.0009531709 0.7058766 0.2119801980 0.9436279
1.0000000 0.0010441784 0.7062672 0.2100990099 0.9442171
1.0000000 0.0011438753 0.7066581 0.2077227723 0.9453643
1.0000000 0.0012530911 0.7070523 0.2045544554 0.9460465
1.0000000 0.0013727347 0.7075137 0.2020792079 0.9468527
1.0000000 0.0015038017 0.7079450 0.1998019802 0.9478450
1.0000000 0.0016473828 0.7083521 0.1975247525 0.9490853
1.0000000 0.0018046729 0.7086933 0.1951485149 0.9505736
1.0000000 0.0019769808 0.7090783 0.1929702970 0.9520000
1.0000000 0.0021657404 0.7093624 0.1902970297 0.9534574
1.0000000 0.0023725226 0.7096457 0.1869306931 0.9554729
1.0000000 0.0025990482 0.7100107 0.1844554455 0.9571473
1.0000000 0.0028472020 0.7103656 0.1808910891 0.9585426
1.0000000 0.0031190494 0.7106449 0.1773267327 0.9596899
1.0000000 0.0034168523 0.7108994 0.1739603960 0.9612713
1.0000000 0.0037430891 0.7111459 0.1715841584 0.9631318
1.0000000 0.0041004746 0.7113008 0.1687128713 0.9643101
1.0000000 0.0044919829 0.7113820 0.1648514851 0.9661395
1.0000000 0.0049208718 0.7112808 0.1605940594 0.9676279
1.0000000 0.0053907105 0.7111101 0.1562376238 0.9686822
1.0000000 0.0059054089 0.7107092 0.1508910891 0.9702946
1.0000000 0.0064692500 0.7101240 0.1446534653 0.9717829
1.0000000 0.0070869259 0.7093062 0.1400990099 0.9740155
1.0000000 0.0077635768 0.7082778 0.1344554455 0.9750698
1.0000000 0.0085048334 0.7071855 0.1295049505 0.9765581
1.0000000 0.0093168644 0.7056601 0.1240594059 0.9786047
1.0000000 0.0102064270 0.7038867 0.1181188119 0.9799690
1.0000000 0.0111809240 0.7018688 0.1108910891 0.9816434
1.0000000 0.0122484647 0.6997002 0.1035643564 0.9834419
1.0000000 0.0134179329 0.6972924 0.0965346535 0.9849612
1.0000000 0.0146990604 0.6947599 0.0883168317 0.9862946
1.0000000 0.0161025084 0.6922597 0.0814851485 0.9875969
1.0000000 0.0176399559 0.6897187 0.0740594059 0.9889302
1.0000000 0.0193241970 0.6870337 0.0667326733 0.9903876
1.0000000 0.0211692473 0.6846097 0.0575247525 0.9916899
1.0000000 0.0231904607 0.6824196 0.0468316832 0.9929612
1.0000000 0.0254046571 0.6801943 0.0377227723 0.9943256
1.0000000 0.0278302622 0.6775725 0.0284158416 0.9951008
1.0000000 0.0304874610 0.6744259 0.0215841584 0.9961240
1.0000000 0.0333983659 0.6701177 0.0165346535 0.9969302
1.0000000 0.0365872004 0.6638295 0.0129702970 0.9977054
1.0000000 0.0400805008 0.6553120 0.0100990099 0.9983256
1.0000000 0.0439073372 0.6455763 0.0073267327 0.9990698
1.0000000 0.0480995552 0.6366205 0.0054455446 0.9993488
1.0000000 0.0526920409 0.6298568 0.0035643564 0.9995969
1.0000000 0.0577230115 0.6256849 0.0024752475 0.9996589
1.0000000 0.0632343329 0.6242616 0.0016831683 0.9996899
1.0000000 0.0692718684 0.6240642 0.0006930693 0.9997209
1.0000000 0.0758858603 0.6240801 0.0000990099 0.9998760
1.0000000 0.0831313479 0.6038349 0.0000000000 1.0000000
1.0000000 0.0910686257 0.5118723 0.0000000000 1.0000000
1.0000000 0.0997637449 0.5000000 0.0000000000 1.0000000
1.0000000 0.1092890633 0.5000000 0.0000000000 1.0000000
1.0000000 0.1197238472 0.5000000 0.0000000000 1.0000000
1.0000000 0.1311549313 0.5000000 0.0000000000 1.0000000
1.0000000 0.1436774411 0.5000000 0.0000000000 1.0000000
1.0000000 0.1573955846 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 = 0.2454545 and lambda
= 0.01469906.
Visualization of Results
Let us apply the resamples ()train ()Chapter 11 ). 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 0.6573490 0.6829457 0.6941860 0.6971720 0.7097859 0.7521606 0
xgb 0.6480697 0.6772066 0.6838130 0.6875713 0.6971659 0.7310538 0
svm 0.6529127 0.6761071 0.6853596 0.6871881 0.6992267 0.7413846 0
glmnet 0.6689001 0.6974288 0.7125911 0.7115699 0.7269936 0.7454141 0
Sens
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
rf 0.06435644 0.07425743 0.08910891 0.08613861 0.09777228 0.1188119 0
xgb 0.53960396 0.59034653 0.60396040 0.60722772 0.62747525 0.6930693 0
svm 0.08415842 0.11509901 0.13861386 0.13623762 0.15346535 0.1930693 0
glmnet 0.10396040 0.15346535 0.16336634 0.16455446 0.17821782 0.2079208 0
Spec
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
rf 0.9674419 0.9813953 0.9844961 0.9845581 0.9891473 0.9953488 0
xgb 0.6217054 0.6558140 0.6651163 0.6658605 0.6825581 0.7085271 0
svm 0.9472868 0.9612403 0.9666667 0.9661395 0.9717054 0.9860465 0
glmnet 0.9488372 0.9627907 0.9658915 0.9658295 0.9717054 0.9782946 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_mhi3.rda" )load ("../data/out/df_test_mhi3.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)
