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Last update:

[1] "Fri May 29 11:58:43 2020"

Fold performances of “SVM MBO No Filter” on the HR Task

Plot

RMSE

Laukiz1

21.17

Oiartzun

28.05

Luiando

9.00

Laukiz2

54.26

(Table) T1 All leaner/filter/task combinations ordered by performance.

Overall leaderboard across all settings, sorted ascending by performance.

Task

Model

Filter

RMSE

SE

NRI

SVM

Info Gain

27.991

19.152

HR-NRI-VI

SVM

Relief

28.067

19.143

VI

SVM

Relief

28.097

19.135

HR-NRI-VI

SVM

Car

28.107

19.126

HR-NRI

SVM

MRMR

28.117

19.113

VI

SVM

Pearson

28.119

19.099

HR-NRI

SVM

CMIM

28.119

19.090

HR

SVM

Info Gain

28.122

19.123

HR

SVM

CMIM

28.122

19.123

NRI-VI

SVM

PCA

28.122

19.123

HR-NRI

SVM

PCA

28.122

19.123

HR-NRI-VI

SVM

PCA

28.122

19.123

NRI-VI

SVM

No Filter

28.122

19.123

HR-NRI

SVM

No Filter

28.122

19.123

HR

SVM

Car

28.123

19.123

HR

SVM

Borda

28.123

19.123

NRI

SVM

PCA

28.123

19.123

HR

SVM

No Filter

28.123

19.123

VI

SVM

No Filter

28.123

19.123

NRI

SVM

No Filter

28.123

19.123

HR-NRI-VI

SVM

No Filter

28.123

19.123

HR

SVM

Pearson

28.124

19.123

NRI-VI

SVM

Car

28.125

19.129

VI

SVM

MRMR

28.125

19.119

HR

SVM

PCA

28.125

19.122

VI

SVM

Info Gain

28.126

19.123

HR-NRI-VI

SVM

MRMR

28.126

19.100

HR

SVM

Relief

28.130

19.114

HR-NRI-VI

SVM

Borda

28.133

19.362

VI

SVM

PCA

28.144

19.091

HR

SVM

MRMR

28.153

19.123

VI

SVM

Borda

28.155

19.183

NRI-VI

SVM

CMIM

28.157

19.077

HR-NRI-VI

SVM

CMIM

28.162

19.071

VI

SVM

CMIM

28.169

19.218

HR-NRI

SVM

Info Gain

28.175

19.131

NRI-VI

SVM

Borda

28.181

19.045

HR-NRI-VI

SVM

Info Gain

28.204

19.023

HR-NRI

SVM

Borda

28.205

19.007

HR-NRI

SVM

Pearson

28.213

19.151

VI

SVM

Car

28.263

19.277

NRI

SVM

Pearson

28.321

18.942

HR-NRI

SVM

Relief

28.337

18.840

HR-NRI

SVM

Car

28.384

18.762

NRI

SVM

Car

28.403

18.744

NRI-VI

SVM

MRMR

28.440

18.753

HR-NRI-VI

SVM

Pearson

28.469

18.640

NRI

SVM

MRMR

28.517

18.607

NRI-VI

SVM

Relief

28.575

18.710

NRI

SVM

CMIM

28.657

18.428

NRI-VI

SVM

Info Gain

28.664

18.299

NRI

SVM

Borda

28.679

18.400

NRI-VI

SVM

Pearson

28.738

18.534

NRI

RF

Car

30.774

16.861

VI

Lasso-MBO

No Filter

31.009

14.714

HR-NRI-VI

XGBoost

Borda

31.052

17.009

HR-NRI

XGBoost

CMIM

31.089

16.576

NRI

Lasso-MBO

No Filter

31.165

15.025

NRI

Ridge-MBO

No Filter

31.165

15.025

NRI-VI

Lasso-MBO

No Filter

31.201

15.089

HR-NRI

Lasso-MBO

No Filter

31.201

15.089

HR-NRI-VI

Lasso-MBO

No Filter

31.201

15.089

NRI-VI

XGBoost

Car

32.082

17.672

NRI-VI

XGBoost

MRMR

32.595

16.279

NRI-VI

XGBoost

CMIM

32.721

15.436

HR-NRI

XGBoost

Relief

32.743

14.094

NRI

RF

PCA

32.966

16.161

NRI

RF

Borda

33.050

14.858

NRI-VI

RF

PCA

33.096

16.001

NRI

XGBoost

Car

33.154

16.403

HR-NRI

RF

PCA

33.255

15.723

NRI-VI

XGBoost

No Filter

33.505

14.599

HR-NRI-VI

RF

PCA

33.588

15.710

HR-NRI-VI

RF

Car

33.779

14.297

HR-NRI

RF

Car

34.044

13.434

NRI

XGBoost

CMIM

34.168

15.571

HR-NRI-VI

XGBoost

MRMR

34.170

14.940

NRI

SVM

Relief

34.177

19.905

NRI

XGBoost

Pearson

34.209

12.246

HR-NRI

XGBoost

Pearson

34.234

13.734

NRI

XGBoost

MRMR

34.393

14.037

HR

RF

Car

34.467

12.604

HR-NRI-VI

XGBoost

Pearson

34.516

14.253

NRI-VI

XGBoost

Info Gain

34.518

15.005

HR-NRI

XGBoost

Borda

34.590

14.887

NRI-VI

XGBoost

Relief

34.709

14.513

NRI

RF

CMIM

34.734

13.733

HR

XGBoost

Borda

34.748

13.572

HR-NRI-VI

RF

CMIM

34.765

13.823

NRI-VI

XGBoost

Pearson

34.771

13.534

HR-NRI-VI

XGBoost

No Filter

34.774

13.804

HR

XGBoost

Relief

34.775

13.317

HR-NRI-VI

XGBoost

CMIM

34.797

14.758

HR-NRI-VI

XGBoost

Car

34.800

14.702

HR

XGBoost

Car

34.806

12.915

NRI

RF

MRMR

34.843

13.937

HR-NRI

RF

MRMR

34.895

13.781

HR-NRI-VI

XGBoost

Info Gain

34.900

13.618

NRI

XGBoost

Info Gain

34.904

15.799

HR-NRI

XGBoost

No Filter

34.963

13.769

NRI-VI

RF

Car

34.996

13.308

HR-NRI-VI

RF

MRMR

35.007

13.634

NRI-VI

RF

CMIM

35.189

14.172

NRI-VI

RF

MRMR

35.223

14.129

HR-NRI-VI

XGBoost

Relief

35.246

12.820

HR

Lasso-MBO

No Filter

35.261

12.243

HR

XGBoost

CMIM

35.276

13.665

HR

XGBoost

Pearson

35.325

13.678

HR

Ridge-MBO

No Filter

35.453

12.655

HR

RF

Borda

35.593

12.949

NRI-VI

XGBoost

Borda

35.608

11.823

NRI

XGBoost

Borda

35.640

12.364

NRI-VI

RF

Borda

35.691

14.051

HR

RF

Info Gain

35.825

13.848

HR-NRI

XGBoost

Info Gain

35.842

14.543

NRI

RF

No Filter

35.868

12.873

HR

XGBoost

MRMR

35.869

13.866

NRI-VI

RF

No Filter

35.999

12.895

NRI

XGBoost

No Filter

36.051

11.604

NRI

XGBoost

Relief

36.062

12.779

HR-NRI

RF

Borda

36.074

13.787

HR-NRI

RF

No Filter

36.222

12.833

HR-NRI

RF

CMIM

36.317

14.144

HR-NRI-VI

RF

No Filter

36.356

12.638

HR-NRI

RF

Info Gain

36.432

13.943

NRI

RF

Info Gain

36.452

13.827

HR-NRI-VI

RF

Info Gain

36.560

13.713

NRI-VI

RF

Info Gain

36.575

13.856

HR-NRI-VI

RF

Borda

36.654

13.050

HR-NRI

RF

Pearson

36.883

13.619

NRI-VI

RF

Pearson

36.903

13.495

HR

RF

Pearson

36.920

14.457

HR-NRI

XGBoost

MRMR

37.010

13.147

NRI

RF

Pearson

37.141

13.266

VI

XGBoost

Relief

37.147

14.262

HR

RF

PCA

37.153

12.329

HR-NRI-VI

RF

Pearson

37.300

13.443

NRI

RF

Relief

37.376

9.425

HR-NRI

XGBoost

Car

37.572

11.101

HR-NRI

RF

Relief

37.690

9.839

VI

RF

MRMR

38.181

12.255

NRI

XGBoost

PCA

38.429

14.982

NRI-VI

RF

Relief

38.498

8.671

HR-NRI-VI

RF

Relief

38.498

8.909

VI

RF

No Filter

38.754

11.271

VI

RF

Borda

38.840

11.237

HR

RF

CMIM

39.687

8.106

VI

XGBoost

Info Gain

39.872

10.880

VI

RF

Info Gain

39.920

9.458

HR

XGBoost

Info Gain

40.070

10.743

VI

RF

CMIM

40.099

9.758

VI

RF

Pearson

40.175

9.959

HR-NRI-VI

XGBoost

PCA

40.185

11.391

VI

RF

Relief

40.449

8.367

NRI-VI

XGBoost

PCA

40.567

10.839

VI

RF

Car

40.581

12.448

VI

RF

PCA

40.719

8.567

HR

RF

Relief

41.656

10.640

VI

XGBoost

CMIM

41.727

9.810

HR

RF

No Filter

41.884

7.806

HR

XGBoost

PCA

42.148

8.077

HR

RF

MRMR

42.312

7.705

HR-NRI

XGBoost

PCA

42.578

8.278

VI

XGBoost

PCA

43.667

8.067

VI

XGBoost

Pearson

44.610

5.783

VI

XGBoost

No Filter

45.689

5.304

VI

XGBoost

Borda

46.041

3.871

VI

XGBoost

MRMR

46.256

9.422

VI

XGBoost

Car

46.399

7.575

HR

XGBoost

No Filter

46.800

3.921

NRI-VI

Ridge-MBO

No Filter

11658468597.678

23316937147.742

HR-NRI-VI

Ridge-MBO

No Filter

12631934180.910

25263868314.206

HR-NRI

Ridge-MBO

No Filter

12650121073.664

25300242099.713

VI

Ridge-MBO

No Filter

49359394487.653

98718788927.687

(Table) T2 Best learner/filter/task combination

Learners: On which task and using which filter did every learner score their best result on?

*CV: L2 penalized regression using the internal 10-fold CV tuning of the glmnet package

*MBO: L2 penalized regression using using MBO for hyperparameter optimization.

Task

Model

Filter

RMSE

SE

NRI

SVM

Info Gain

27.991

19.152

NRI

RF

Car

30.774

16.861

VI

Lasso-MBO

No Filter

31.009

14.714

HR-NRI-VI

XGBoost

Borda

31.052

17.009

NRI

Ridge-MBO

No Filter

31.165

15.025

(Table) T3 All leaner/filter/task combinations ordered descending by performance.

Overall leaderboard across all settings, sorted descending by performance.

Task

Model

Filter

RMSE

VI

Ridge-MBO

No Filter

49359394487.653

HR-NRI

Ridge-MBO

No Filter

12650121073.664

HR-NRI-VI

Ridge-MBO

No Filter

12631934180.910

NRI-VI

Ridge-MBO

No Filter

11658468597.678

HR

XGBoost

No Filter

46.800

VI

XGBoost

Car

46.399

VI

XGBoost

MRMR

46.256

VI

XGBoost

Borda

46.041

VI

XGBoost

No Filter

45.689

VI

XGBoost

Pearson

44.610

(Plot) P1 Best learner/filter combs for all tasks

Version Author Date
e20d376 pat-s 2020-04-29
07fb043 pat-s 2020-04-18
1d1bee4 pat-s 2020-04-18
6c42b7c pat-s 2020-04-18
544e288 pat-s 2020-04-12
f59d02a pat-s 2020-03-05
2ee982d pat-s 2020-03-05
274a918 pat-s 2020-02-25
b25e779 pat-s 2020-01-10
7f9507f pat-s 2019-12-10
482a158 pat-s 2019-11-01
becf5ea pat-s 2019-11-01
bd7c7f5 pat-s 2019-10-31
62ff96f pat-s 2019-10-07
a947654 pat-s 2019-10-02
49da171 pat-s 2019-09-22
41aae14 pat-s 2019-09-12
b181c52 pat-s 2019-09-02
8e7e4fe pat-s 2019-09-01
7582c67 pat-s 2019-08-31
abd531f pat-s 2019-08-31

(Plot) P2 Scatterplots of filter methods vs. no filter for each learner and task

Showing the final effect of applying feature selection to a learner for each task. All filters are colored in the same way whereas using “no filter” appears in a different color.

Version Author Date
e20d376 pat-s 2020-04-29
1d1bee4 pat-s 2020-04-18
6c42b7c pat-s 2020-04-18
544e288 pat-s 2020-04-12
f59d02a pat-s 2020-03-05
2ee982d pat-s 2020-03-05
274a918 pat-s 2020-02-25
b25e779 pat-s 2020-01-10
7f9507f pat-s 2019-12-10

(Plot) P3 Scatterplots of filter methods vs. Borda for each learner and task

Showing the final effect of applying feature selection to a learner for each task. All filters are summarized into a a single color whereas the “Borda” filter appears in its own color.

Version Author Date
e20d376 pat-s 2020-04-29
1d1bee4 pat-s 2020-04-18
6c42b7c pat-s 2020-04-18
544e288 pat-s 2020-04-12
f59d02a pat-s 2020-03-05
2ee982d pat-s 2020-03-05
776b35f pat-s 2020-03-03
274a918 pat-s 2020-02-25
b25e779 pat-s 2020-01-10
7f9507f pat-s 2019-12-10

(Table) T4 Number of features selected during tuning

The model/task combinations which were selected relate to the best performance of the respective algorithm on the HR-NRI-VI task in the overall benchmark.

Learner

Plot

Features (\%)

\#

RMSE

\multirow{4}{*}{\specialcell{RF \\ Car}}

Laukiz1

0.00442770793

3

27.47555

Oiartzun

0.00004284398

1

37.20174

Luiando

0.00001759137

1

36.97731

Laukiz2

0.03037475841

14

15.38889

\midrule\multirow{4}{*}{\specialcell{XGB \\ Borda}}

Laukiz1

0.00604085450

4

14.99858

Oiartzun

0.68226628614

340

36.14989

Luiando

0.99512538539

300

38.18847

Laukiz2

0.99975780141

451

29.62486

\midrule\multirow{4}{*}{\specialcell{SVM \\ Relief}}

Laukiz1

0.00017322853

1

35.50464

Oiartzun

0.71146238948

354

37.48157

Luiando

0.70671610551

213

14.89400

Laukiz2

0.94695622557

428

37.18944

Aggregated mean and standard deviation:

Learner

Mean (Features (%))

SD (Features (%))

RF Car

0.008715725

0.01458741

SVM Relief

0.591326987

0.40974874

XGBoost Borda

0.670797582

0.46741715


R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRblas.so
LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.4.0     dplyr_0.8.3       mlr_2.17.0.9001  
 [4] ParamHelpers_1.12 here_0.1          ggpubr_0.1.6     
 [7] magrittr_1.5      ggrepel_0.8.0     ggsci_2.9        
[10] ggbeeswarm_0.7.0  ggplot2_3.3.0     flextable_0.5.8  
[13] xtable_1.8-3      tidyselect_0.2.5  drake_7.12.0     

loaded via a namespace (and not attached):
 [1] fs_1.4.1           filelock_1.0.2     RColorBrewer_1.1-2
 [4] progress_1.2.0     httr_1.4.0         rprojroot_1.3-2   
 [7] tools_3.6.1        backports_1.1.5    R6_2.4.1          
[10] vipor_0.4.5        lazyeval_0.2.1     colorspace_1.4-0  
[13] withr_2.1.2        prettyunits_1.0.2  mco_1.0-15.1      
[16] compiler_3.6.1     git2r_0.26.1       parallelMap_1.4   
[19] cli_2.0.2          DiceKriging_1.5.6  xml2_1.2.2        
[22] plotly_4.8.0       officer_0.3.7      labeling_0.3      
[25] scales_1.1.0       checkmate_2.0.0    plot3D_1.1.1      
[28] systemfonts_0.1.1  stringr_1.4.0      digest_0.6.25     
[31] txtq_0.1.4         rmarkdown_1.13     R.utils_2.8.0     
[34] smoof_1.5.1        base64enc_0.1-3    pkgconfig_2.0.3   
[37] htmltools_0.3.6    lhs_1.0.1          htmlwidgets_1.3   
[40] rlang_0.4.5        BBmisc_1.11        farver_2.0.3      
[43] mlrMBO_1.1.2       jsonlite_1.6       zip_2.0.4         
[46] R.oo_1.23.0        Matrix_1.2-15      Rcpp_1.0.3        
[49] munsell_0.5.0      fansi_0.4.1        gdtools_0.2.1     
[52] lifecycle_0.1.0    R.methodsS3_1.7.1  stringi_1.3.1     
[55] whisker_0.3-2      yaml_2.2.0         storr_1.2.1       
[58] RJSONIO_1.3-1.1    grid_3.6.1         misc3d_0.8-4      
[61] parallel_3.6.1     promises_1.0.1     crayon_1.3.4      
[64] lattice_0.20-38    splines_3.6.1      hms_0.5.3         
[67] knitr_1.23         pillar_1.4.3       igraph_1.2.4.1    
[70] uuid_0.1-2         base64url_1.4      fastmatch_1.1-0   
[73] glue_1.4.0         evaluate_0.13      data.table_1.12.8 
[76] vctrs_0.2.4        httpuv_1.4.5.1     gtable_0.3.0      
[79] purrr_0.3.4        tidyr_1.0.0        assertthat_0.2.1  
[82] xfun_0.5           later_1.0.0        survival_2.43-3   
[85] viridisLite_0.3.0  tibble_2.1.3       beeswarm_0.2.3    
[88] workflowr_1.6.1    ellipsis_0.3.0