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Last update:

[1] "Thu Sep  2 09:34:58 2021"

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

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

Overall leaderboard across all settings, sorted ascending by performance.

(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.

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

Overall leaderboard across all settings, sorted descending by performance.

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

Version Author Date
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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
c56ffc4 pat-s 2021-08-18
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b24002a pat-s 2021-06-22
2c8ad62 pat-s 2021-06-11
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2ee982d pat-s 2020-03-05
274a918 pat-s 2020-02-25
b25e779 pat-s 2020-01-10
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(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
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
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(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.

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):

RF

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
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[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
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[13] usethis_2.0.0     magrittr_2.0.1    drake_7.13.2     

loaded via a namespace (and not attached):
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 [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   
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 [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         
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 [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         
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 [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          
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[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     
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[109] tcltk_4.0.4