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Last update:
[1] "Tue Aug 24 21:42:45 2021"
Fold performances of “SVM MBO No Filter” on the HR Task
Overall leaderboard across all settings, sorted ascending by performance.
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.
Overall leaderboard across all settings, sorted descending by performance.
Version | Author | Date |
---|---|---|
c56ffc4 | pat-s | 2021-08-18 |
d4267fd | pat-s | 2021-06-24 |
b24002a | pat-s | 2021-06-22 |
bf76e50 | pat-s | 2021-06-22 |
2c8ad62 | pat-s | 2021-06-11 |
d63a11c | pat-s | 2021-04-06 |
0bb65fb | pat-s | 2021-03-29 |
b2ac43b | pat-s | 2021-02-20 |
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 |
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 |
---|---|---|
c56ffc4 | pat-s | 2021-08-18 |
d4267fd | pat-s | 2021-06-24 |
b24002a | pat-s | 2021-06-22 |
2c8ad62 | pat-s | 2021-06-11 |
d63a11c | pat-s | 2021-04-06 |
0bb65fb | pat-s | 2021-03-29 |
b2ac43b | pat-s | 2021-02-20 |
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 |
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 |
---|---|---|
c56ffc4 | pat-s | 2021-08-18 |
d4267fd | pat-s | 2021-06-24 |
b24002a | pat-s | 2021-06-22 |
2c8ad62 | pat-s | 2021-06-11 |
6bec2b4 | pat-s | 2021-04-09 |
d63a11c | pat-s | 2021-04-06 |
0bb65fb | pat-s | 2021-03-29 |
b2ac43b | pat-s | 2021-02-20 |
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 |
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.
Fold IDs are different for each learner, i.e. a specific plot does not always resolve to “fold 1” for each learner. See bmr_inspect_tune[["results"]][["hr_nri_vi"]][["RF MBO Relief"]][["pred"]][["instance"]][["test.inds"]]
.
Thus, we need to manually label the fold IDs to plot names for each learner.
Example for RF on fold 1 (Luiando):
bmr_inspect_tune[["results"]][["hr_nri_vi"]][["RF MBO Relief"]][["extract"]][[1]][["mbo.result"]][["x"]][["fw.perc"]]
: 99.972RF
SVM
XGBoost
Aggregated mean and standard deviation:
R version 4.0.4 (2021-02-15)
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-4.0.4-udi7a3ahhtokdcoyqdbndhebeupt7hid/rlib/R/lib/libRblas.so
LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-4.0.4-udi7a3ahhtokdcoyqdbndhebeupt7hid/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 datasets utils methods base
other attached packages:
[1] forcats_0.5.1 dplyr_1.0.4 mlr_2.19.0.9000 ParamHelpers_1.14
[5] here_1.0.1 ggpubr_0.4.0 ggrepel_0.9.1 ggsci_2.9
[9] ggbeeswarm_0.7.0 ggplot2_3.3.3 flextable_0.6.3 xtable_1.8-4
[13] usethis_2.0.0 magrittr_2.0.1 drake_7.13.2
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ggsignif_0.6.0 smoof_1.6.0.2
[4] ellipsis_0.3.1 rio_0.5.16 rprojroot_2.0.2
[7] base64enc_0.1-3 fs_1.5.0 rstudioapi_0.13
[10] farver_2.0.3 DT_0.17 xml2_1.3.2
[13] splines_4.0.4 R.methodsS3_1.8.1 knitr_1.31
[16] mco_1.15.6 jsonlite_1.7.2 workflowr_1.6.2
[19] broom_0.7.4 R.oo_1.24.0 mlrMBO_1.1.5
[22] compiler_4.0.4 httr_1.4.2 backports_1.2.1
[25] Matrix_1.3-2 lazyeval_0.2.2 cli_2.4.0
[28] later_1.1.0.1 htmltools_0.5.1.1 prettyunits_1.1.1
[31] tools_4.0.4 igraph_1.2.6 misc3d_0.9-0
[34] gtable_0.3.0 glue_1.4.2 fastmatch_1.1-0
[37] Rcpp_1.0.6 parallelMap_1.5.0 carData_3.0-4
[40] cellranger_1.1.0 vctrs_0.3.6 RJSONIO_1.3-1.4
[43] crosstalk_1.1.1 xfun_0.20 stringr_1.4.0
[46] openxlsx_4.2.3 lifecycle_0.2.0 renv_0.13.2
[49] rstatix_0.6.0 scales_1.1.1 hms_1.0.0
[52] promises_1.1.1 parallel_4.0.4 plot3D_1.3
[55] RColorBrewer_1.1-2 BBmisc_1.11 yaml_2.2.1
[58] curl_4.3 gdtools_0.2.3 DiceKriging_1.5.8
[61] stringi_1.5.3 highr_0.8 checkmate_2.0.0
[64] lhs_1.1.1 filelock_1.0.2 zip_2.1.1
[67] storr_1.2.5 rlang_0.4.10 pkgconfig_2.0.3
[70] systemfonts_1.0.0 evaluate_0.14 lattice_0.20-41
[73] ggbeeswarm2_0.7.0 purrr_0.3.4 labeling_0.4.2
[76] htmlwidgets_1.5.3 tidyselect_1.1.0 R6_2.5.0
[79] generics_0.1.0 base64url_1.4 txtq_0.2.3
[82] pillar_1.4.7 haven_2.3.1 whisker_0.4
[85] foreign_0.8-81 withr_2.4.1 abind_1.4-5
[88] survival_3.2-7 tibble_3.0.6 crayon_1.4.0
[91] car_3.0-10 uuid_0.1-4 plotly_4.9.3
[94] rmarkdown_2.6 officer_0.3.16 progress_1.2.2
[97] grid_4.0.4 readxl_1.3.1 data.table_1.13.6
[100] git2r_0.28.0 digest_0.6.27 tidyr_1.1.2
[103] httpuv_1.5.5 R.utils_2.10.1 munsell_0.5.0
[106] beeswarm_0.2.3 viridisLite_0.3.0 vipor_0.4.5
[109] tcltk_4.0.4