title: "model_perfs_training_set
"
author: "Benny Salo"
date: "2019-02-14"
output: github_document
Here we extract the model performances in the seperate 25 x 4 folds in the training data.
These are numbers that we can make public and therfore store in /data
devtools::wd()
trained_mods_all <- readRDS("not_public/trained_mods_all.rds")
trained_mods_all1000 <- readRDS("not_public/trained_mods_all1000.rds")
Creating model_AUCs_training_set
Extract the model performances in the 25 x 4 folds in the training data
model_perfs_training_set <- caret::resamples(trained_mods_all)
model_perfs_training_set1000 <- caret::resamples(trained_mods_all1000)
Save and make available via /data
devtools::use_data(model_perfs_training_set, overwrite = TRUE)
## <U+2714> Saving 'model_perfs_training_set' to 'data/model_perfs_training_set.rda'
devtools::use_data(model_perfs_training_set1000, overwrite = TRUE)
## <U+2714> Saving 'model_perfs_training_set1000' to 'data/model_perfs_training_set1000.rda'
sessionInfo()
## R version 3.5.2 (2018-12-20)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows >= 8 x64 (build 9200)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Swedish_Finland.1252 LC_CTYPE=Swedish_Finland.1252
## [3] LC_MONETARY=Swedish_Finland.1252 LC_NUMERIC=C
## [5] LC_TIME=Swedish_Finland.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] recidivismsl_0.0.0.9000 assertthat_0.2.0
## [3] caret_6.0-81 lattice_0.20-38
## [5] bindrcpp_0.2.2 ggplot2_3.1.0
## [7] dplyr_0.7.8 testthat_2.0.1
## [9] purrr_0.2.5 magrittr_1.5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-137 fs_1.2.6
## [3] xopen_1.0.0 usethis_1.4.0
## [5] lubridate_1.7.4 devtools_2.0.1
## [7] rprojroot_1.3-2 tools_3.5.2
## [9] backports_1.1.3 utf8_1.1.4
## [11] R6_2.3.0 rpart_4.1-13
## [13] lazyeval_0.2.1 colorspace_1.4-0
## [15] nnet_7.3-12 withr_2.1.2
## [17] ResourceSelection_0.3-4 tidyselect_0.2.5
## [19] prettyunits_1.0.2 processx_3.2.1
## [21] compiler_3.5.2 glmnet_2.0-16
## [23] cli_1.0.1 xml2_1.2.0
## [25] desc_1.2.0 scales_1.0.0
## [27] randomForest_4.6-14 readr_1.3.1
## [29] callr_3.1.1 commonmark_1.7
## [31] stringr_1.3.1 digest_0.6.18
## [33] pkgconfig_2.0.2 sessioninfo_1.1.1
## [35] highr_0.7 rlang_0.3.1
## [37] ggthemes_4.0.1 rstudioapi_0.9.0
## [39] bindr_0.1.1 generics_0.0.2
## [41] ModelMetrics_1.2.2 Matrix_1.2-15
## [43] Rcpp_1.0.0 munsell_0.5.0
## [45] fansi_0.4.0 furniture_1.8.7
## [47] stringi_1.2.4 pROC_1.13.0
## [49] yaml_2.2.0 MASS_7.3-51.1
## [51] pkgbuild_1.0.2 plyr_1.8.4
## [53] recipes_0.1.4 grid_3.5.2
## [55] forcats_0.3.0 crayon_1.3.4
## [57] splines_3.5.2 hms_0.4.2
## [59] knitr_1.21 ps_1.3.0
## [61] pillar_1.3.1 reshape2_1.4.3
## [63] codetools_0.2-15 clisymbols_1.2.0
## [65] stats4_3.5.2 pkgload_1.0.2
## [67] glue_1.3.0 evaluate_0.12
## [69] data.table_1.12.0 remotes_2.0.2
## [71] foreach_1.4.4 gtable_0.2.0
## [73] rcmdcheck_1.3.2 tidyr_0.8.2
## [75] xfun_0.4 gower_0.1.2
## [77] prodlim_2018.04.18 roxygen2_6.1.1
## [79] class_7.3-14 survival_2.43-3
## [81] timeDate_3043.102 tibble_2.0.1
## [83] iterators_1.0.10 memoise_1.1.0
## [85] lava_1.6.4 ipred_0.9-8
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.