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Correlation of filter methods

Spearman’s rank correlation is used because rankings are compared.

VI

Filter methods amongst each other

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

Version Author Date
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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
09f6292 pat-s 2019-06-30

NRI

Version Author Date
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

HR

Version Author Date
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

Number of bins of FSelectorRcpp::information.gain()

Analyzing the effect of a different nbins value on the filter values of filter “Information Gain”.

  • Lower correlation / highest difference: 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.

Version Author Date
8b5e422 pat-s 2020-08-05
1054020 pat-s 2020-02-17
7fae583 pat-s 2019-12-21
09f6292 pat-s 2019-06-30

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:
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 [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_7.13.1    

loaded via a namespace (and not attached):
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 [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] pillar_1.4.7      later_1.1.0.1     R.utils_2.10.1    txtq_0.2.3       
[17] withr_2.4.1       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_0.13.0      
[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_2.3.0         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] assertthat_0.2.1  rmarkdown_2.6     rstudioapi_0.13   R6_2.5.0         
[57] igraph_1.2.6      compiler_4.0.4    git2r_0.28.0