Last updated: 2021-11-02
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Knit directory: 2019-feature-selection/
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Spearman’s rank correlation is used because rankings are compared.
The idea behind is was to analyze the correlation between filter rankings. We only wanted to included filters which have a somewhat unique ranking. Otherwise, when creating ensemble filters, certain filters would implicitly be weighted more than others.
Takeaway:
Only use one of “information gain”, “gain ratio”, “sym uncert”
Either use Spearman or Pearson correlation
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| 0d91238 | pat-s | 2021-11-02 |
| 6c153bc | pat-s | 2021-04-05 |
| 8b5e422 | pat-s | 2020-08-05 |
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| 27d4ac5 | pat-s | 2019-09-02 |
| 7582c67 | pat-s | 2019-08-31 |
| f9e682f | pat-s | 2019-08-29 |
| dc1a55d | pat-s | 2019-08-12 |
| 3a44a95 | pat-s | 2019-07-10 |
| 869c409 | pat-s | 2019-07-02 |
| 09f6292 | pat-s | 2019-06-30 |
| Version | Author | Date |
|---|---|---|
| 0d91238 | pat-s | 2021-11-02 |
| 6c153bc | pat-s | 2021-04-05 |
| 8b5e422 | pat-s | 2020-08-05 |
| 1054020 | pat-s | 2020-02-17 |
| 25f06fc | pat-s | 2020-01-15 |
| 7fae583 | pat-s | 2019-12-21 |
| 27d4ac5 | pat-s | 2019-09-02 |
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| dc1a55d | pat-s | 2019-08-12 |
| 3a44a95 | pat-s | 2019-07-10 |
| 869c409 | pat-s | 2019-07-02 |
| Version | Author | Date |
|---|---|---|
| 0d91238 | pat-s | 2021-11-02 |
| 6c153bc | pat-s | 2021-04-05 |
| 8b5e422 | pat-s | 2020-08-05 |
| 1054020 | pat-s | 2020-02-17 |
| 25f06fc | pat-s | 2020-01-15 |
| 7fae583 | pat-s | 2019-12-21 |
| 27d4ac5 | pat-s | 2019-09-02 |
| 7582c67 | pat-s | 2019-08-31 |
| f9e682f | pat-s | 2019-08-29 |
| dc1a55d | pat-s | 2019-08-12 |
| 3a44a95 | pat-s | 2019-07-10 |
| 869c409 | pat-s | 2019-07-02 |
| 09f6292 | pat-s | 2019-06-30 |
FSelectorRcpp::information.gain()Analyzing the effect of a different nbins value on the filter values of filter “Information Gain”.
nbins = 5 vs. nbins = 30-> We decided to use with nbins = 10 in the analysis.
The hidden default of nbins when setting equal = TRUE in FSelectorRcpp::information_gain() is 5.
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
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[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] fs_1.5.0 glue_1.4.2 purrr_0.3.4 ggcorrplot_0.1.3
[5] ggplot2_3.3.3 tidyr_1.1.2 dplyr_1.0.4 usethis_2.0.0
[9] magrittr_2.0.1 drake_7.13.2
loaded via a namespace (and not attached):
[1] storr_1.2.5 progress_1.2.2 tidyselect_1.1.0 xfun_0.20
[5] reshape2_1.4.4 colorspace_2.0-0 vctrs_0.3.6 generics_0.1.0
[9] htmltools_0.5.1.1 yaml_2.2.1 rlang_0.4.10 R.oo_1.24.0
[13] later_1.1.0.1 pillar_1.4.7 txtq_0.2.3 withr_2.4.1
[17] R.utils_2.10.1 plyr_1.8.6 lifecycle_0.2.0 stringr_1.4.0
[21] munsell_0.5.0 gtable_0.3.0 workflowr_1.6.2 R.methodsS3_1.8.1
[25] evaluate_0.14 labeling_0.4.2 knitr_1.31 httpuv_1.5.5
[29] parallel_4.0.4 highr_0.8 Rcpp_1.0.6 renv_0.13.2
[33] backports_1.2.1 promises_1.1.1 scales_1.1.1 filelock_1.0.2
[37] farver_2.0.3 hms_1.0.0 digest_0.6.27 stringi_1.5.3
[41] rprojroot_2.0.2 grid_4.0.4 cli_2.4.0 tools_4.0.4
[45] base64url_1.4 tibble_3.0.6 crayon_1.4.0 whisker_0.4
[49] pkgconfig_2.0.3 ellipsis_0.3.1 data.table_1.13.6 prettyunits_1.1.1
[53] rmarkdown_2.6 rstudioapi_0.13 R6_2.5.0 igraph_1.2.6
[57] compiler_4.0.4 git2r_0.28.0