This is a small package offering a minimalistic but flexible framework for creating, managing and training multiple caret models with a bare minimum of code.

modelgrid can be installed from CRAN with install.packages('modelgrid'). If you want the development version then install directly from GitHub:

# install.packages("devtools")
devtools::install_github("smaakage85/modelgrid")

First, pre-allocate an empty model grid with the constructor function model_grid.

library(modelgrid)
mg <- model_grid()

mg
#> $shared_settings
#> list()
#> 
#> $models
#> list()
#> 
#> $model_fits
#> list()
#> 
#> attr(,"class")
#> [1] "model_grid"

As you see, a model_grid has three components:

• shared_settings: settings to be shared across models. Obvious choices include the target variable, features and resampling scheme.
• models: settings that uniquely identify the indvidual models.
• model_fits: contains the fitted models, once the model_grid has been trained.

Next, decide what settings you want to be shared by the models constituting the model_grid.

library(dplyr)
library(lattice)
library(caret)
data(GermanCredit)

mg <-
  mg %>%
  share_settings(
    y = GermanCredit[["Class"]],
    x = GermanCredit %>% select(-Class),
    metric = "ROC",
    trControl = trainControl(
      method = "cv",
      number = 5,
      summaryFunction = twoClassSummary,
      classProbs = TRUE
      )
  )

Our first model candidate will be a simple Random Forest configuration.

mg <-
  mg %>%
  add_model(
    model_name = "Funky Forest",
    method = "rf",
    tuneLength = 5
  )

Let us also give an eXtreme Gradient Boosting model a shot.

mg <-
  mg %>%
  add_model(
    model_name = "Big Boost",
    method = "xgbTree",
    nthread = 8
  )

That's it. We are all set to train our first very own (extremely simple) model grid.

mg <- train(mg)

Visualize performance statistics of final models.

mg$model_fits %>%
  resamples(.) %>%
  bwplot(.)

You want to know more about all of the exciting features of the model_grid? Take a look at the vignette (: