trainStack: Train a stacked model using caret

In sgreben/caretStack: Stacking models using caret

Description

Train a stacked model using caret

Usage

trainStack(x, y, layers, folds, verbose = F)

Arguments

x	Predictors
y	Response
layers	Stack layers. A list of lists of models.
folds	CV folds, as created by caret::createFolds
verbose	Output progress messages

Examples

stackLayers <- list(
 list(
   gbm2 = list(
     parallel = 4,
     params = list(
       method = "gbm",
       tuneGrid = expand.grid(
         n.trees = 300,
         interaction.depth = 2,
         shrinkage = 0.1,
         n.minobsinnode = 10
       ),
       trControl = trainControl(method = "none")
     )
   ),
   gbm10 = list(
     parallel = 4,
     params = list(
       method = "gbm",
       tuneGrid = expand.grid(
         n.trees = 300,
         interaction.depth = 10,
         shrinkage = 0.1,
         n.minobsinnode = 10
       ),
       trControl = trainControl(method = "none")
     )
   )
  ),
 list(
    gbm2 = list(
      parallel = 4,
      params = list(
        method = "gbm",
        tuneGrid = expand.grid(
          n.trees = 300,
          interaction.depth = 2,
          shrinkage = 0.1,
          n.minobsinnode = 10
        ),
        trControl = trainControl(method = "none")
      )
    )
)
)

folds <- caret::createFolds(x, 10)
stackModel <- trainStack(x, y, stackLayers, folds)

sgreben/caretStack documentation built on May 30, 2019, 7:18 p.m.