Código en R de mi participación en la competición de Driven Data "Pump it Up: Data Mining the Water Table", para la asignatura de Minería de Datos: Preprocesamiento y Clasificación, usando sólo SVM (Support Vector Machines).
La puntuación final obtenida es: 0.7969.
El código es reproducible y se ha realizado con esta sessionInfo:
> sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] e1071_1.7-3 tictoc_1.0 caret_6.0-85 ggplot2_3.2.1
[5] mice_3.7.0 lattice_0.20-38 VIM_5.1.0 data.table_1.12.8
[9] colorspace_1.4-1 dplyr_0.8.4
loaded via a namespace (and not attached):
[1] tidyr_1.0.2 splines_3.6.2 foreach_1.4.7 carData_3.0-3
[5] prodlim_2019.11.13 assertthat_0.2.1 stats4_3.6.2 sp_1.3-2
[9] cellranger_1.1.0 robustbase_0.93-5 ipred_0.9-9 pillar_1.4.3
[13] backports_1.1.5 glue_1.3.1 pROC_1.16.1 minqa_1.2.4
[17] recipes_0.1.9 Matrix_1.2-18 plyr_1.8.5 timeDate_3043.102
[21] pkgconfig_2.0.3 broom_0.5.4 haven_2.2.0 purrr_0.3.3
[25] scales_1.1.0 ranger_0.12.1 openxlsx_4.1.4 gower_0.2.1
[29] lava_1.6.6 rio_0.5.16 lme4_1.1-21 tibble_2.1.3
[33] generics_0.0.2 car_3.0-6 withr_2.1.2 pan_1.6
[37] nnet_7.3-12 lazyeval_0.2.2 survival_3.1-8 magrittr_1.5
[41] crayon_1.3.4 readxl_1.3.1 mitml_0.3-7 laeken_0.5.1
[45] nlme_3.1-143 MASS_7.3-51.5 forcats_0.4.0 foreign_0.8-75
[49] class_7.3-15 tools_3.6.2 hms_0.5.3 lifecycle_0.1.0
[53] stringr_1.4.0 munsell_0.5.0 zip_2.0.4 compiler_3.6.2
[57] vcd_1.4-5 rlang_0.4.4 nloptr_1.2.1 iterators_1.0.12
[61] rstudioapi_0.10 boot_1.3-24 ModelMetrics_1.2.2.1 gtable_0.3.0
[65] codetools_0.2-16 abind_1.4-5 curl_4.3 reshape2_1.4.3
[69] R6_2.4.1 lubridate_1.7.4 zoo_1.8-7 jomo_2.6-10
[73] stringi_1.4.5 parallel_3.6.2 Rcpp_1.0.3 vctrs_0.2.2
[77] rpart_4.1-15 DEoptimR_1.0-8 tidyselect_1.0.0 lmtest_0.9-37