Notes and code for the workshop "Rule-Based Models for Regression and Classification” presented at the 2016 Open Data Science Conference in Boston.
The packages are used to create the majority of the slides:
Other packages are needed to create all the slides, especially to compare against the other models. The sessionInfo from when I ran the code to create the slides is:
> sessionInfo()
R Under development (unstable) (2016-04-08 r70447)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.5 (Yosemite)
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] parallel splines grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] car_2.1-2 reshape2_1.4.1
[3] doMC_1.3.4 iterators_1.0.8
[5] foreach_1.4.3 Hmisc_3.17-3
[7] Formula_1.2-1 ggmap_2.6.1
[9] RWeka_0.4-26 kernlab_0.9-24
[11] ranger_0.4.0 gbm_2.1.1
[13] survival_2.38-3 partykit_1.0-5
[15] rpart_4.1-10 Cubist_0.0.18
[17] pROC_1.8 MASS_7.3-45
[19] AppliedPredictiveModeling_1.1-6 caret_6.0-69
[21] ggplot2_2.1.0 lattice_0.20-33
[23] knitr_1.12.3
loaded via a namespace (and not attached):
[1] Rcpp_0.12.4 png_0.1-7 digest_0.6.9
[4] plyr_1.8.3 acepack_1.3-3.3 MatrixModels_0.4-1
[7] stats4_3.4.0 evaluate_0.8.3 highr_0.5.1
[10] RWekajars_3.7.13-1 RgoogleMaps_1.2.0.7 minqa_1.2.4
[13] geosphere_1.5-1 SparseM_1.7 nloptr_1.0.4
[16] Matrix_1.2-4 labeling_0.3 proto_0.3-10
[19] lme4_1.1-11 stringr_1.0.0 foreign_0.8-66
[22] munsell_0.4.3 mgcv_1.8-12 nnet_7.3-12
[25] gridExtra_2.2.1 CORElearn_1.47.1 codetools_0.2-14
[28] nlme_3.1-126 gtable_0.2.0 magrittr_1.5
[31] formatR_1.3 scales_0.4.0 stringi_1.0-1
[34] mapproj_1.2-4 sp_1.2-2 latticeExtra_0.6-28
[37] rjson_0.2.15 RColorBrewer_1.1-2 tools_3.4.0
[40] RJSONIO_1.3-0 maps_3.1.0 jpeg_0.1-8
[43] pbkrtest_0.4-6 colorspace_1.2-6 cluster_2.0.3
[46] rJava_0.9-8 quantreg_5.21Two main data sets are used to compute the models. One is in the caret package and the other is a credit scoring dataset from Gaston Sanchez by way of this blog post by Sergio Venturini:
library(RCurl)
url <- "https://raw.githubusercontent.com/gastonstat/CreditScoring/master/CleanCreditScoring.csv"
cs_data <- getURL(url)
cs_data <- read.csv(textConnection(cs_data))
## Remove some predictors that are discrete versions of existing fields
cs_data <- cs_data[, !grepl("R$", names(cs_data))]
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