Wsrtjtyk

I am trying to build a regression model using the Boston Housing data using the caret package. The code is as follows

library(tidyverse)

library(ggplot2)

library(lubridate)

library(broom)

library(caret)

library(xgboost)



#list.files()



options(scipen = 999)



library(MASS)



data_model <- Boston

data_model <- as.data.frame(data_model)



# based on this link https://stackoverflow.com/questions/51762536/r-xgboost-on-caret-attempts-to-perform-classification-instead-of-regression

data_model$medv <- as.double(data_model$medv)

data_model$zn <- as.double(data_model$zn)

xgb_grid_1 = expand.grid(

  nrounds = 1000,

  max_depth = c(2, 4, 6, 8, 10),

  eta=c(0.5, 0.1, 0.07),

  gamma = 0.01,

  colsample_bytree=0.5,

  min_child_weight=1,

  subsample=0.5

)



xgb_trcontrol_1 = trainControl(

  method = "cv",

  number = 5,

  allowParallel = TRUE

)





xgb_train_1 = train(

  x = data_model %>% dplyr::select(-medv) %>% as.matrix(),

  y = as.matrix(data_model$medv),

  trControl = xgb_trcontrol_1,

  tuneGrid = xgb_grid_1,

  method = "xgbTree",

  metric = 'RMSE'

)



sessionInfo()

But when I run the train() function I get the error Error: Metric RMSE not applicable for classification models. Then I tried to change variables that were integers to double as suggested by this link. I still seem to get the same error. Am I missing out on an extra parameter that should take care of this? Thank You in advance! I have also included my session information below in case there is version conflict that I am not aware of.

R version 3.4.0 (2017-04-21)

Platform: x86_64-apple-darwin15.6.0 (64-bit)

Running under: macOS  10.14



Matrix products: default

BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib

LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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] stats     graphics  grDevices utils     datasets  methods   base     



other attached packages:

 [1] MASS_7.3-47        bindrcpp_0.2.2     xgboost_0.71.2     caret_6.0-81       lattice_0.20-35    broom_0.4.2        lubridate_1.6.0    dplyr_0.7.8        purrr_0.2.3       

[10] readr_1.1.1        tidyr_0.7.2        tibble_1.4.2       ggplot2_2.2.1.9000 tidyverse_1.1.1   



loaded via a namespace (and not attached):

 [1] Rcpp_1.0.0           class_7.3-14         utf8_1.1.3           assertthat_0.2.0     ipred_0.9-6          psych_1.7.5          foreach_1.4.3        R6_2.2.2            

 [9] cellranger_1.1.0     plyr_1.8.4           stats4_3.4.0         httr_1.3.1           pillar_1.2.1         rlang_0.3.0.1        lazyeval_0.2.1       readxl_1.0.0        

[17] rstudioapi_0.7       data.table_1.10.4    rpart_4.1-11         Matrix_1.2-9         splines_3.4.0        gower_0.1.2          stringr_1.3.0        foreign_0.8-67      

[25] munsell_0.4.3        compiler_3.4.0       modelr_0.1.1         pkgconfig_2.0.1      mnormt_1.5-5         nnet_7.3-12          tidyselect_0.2.5     prodlim_2018.04.18  

[33] codetools_0.2-15     crayon_1.3.4         withr_2.1.2          recipes_0.1.4        ModelMetrics_1.1.0   grid_3.4.0           nlme_3.1-131         jsonlite_1.5        

[41] gtable_0.2.0         magrittr_1.5         waterfalls_0.1.2     scales_0.5.0.9000    cli_1.0.0            stringi_1.1.7        reshape2_1.4.3       timeDate_3012.100   

[49] xml2_1.2.0           generics_0.0.1       lava_1.6.1           iterators_1.0.8      tools_3.4.0          forcats_0.2.0        glue_1.3.0           hms_0.3             

[57] parallel_3.4.0       survival_2.41-3      colorspace_1.3-2     xgboostExplainer_0.1 rvest_0.3.2          bindr_0.1.1          haven_1.1.0  

You have already converted data_model$zn to double. So, just remove as.matrix in the parameter y = as.matrix(data_model$medv)