caretEnsemble (version 4.0.1)

predict.caretStack: Make predictions from a caretStack

Description

Make predictions from a caretStack. This function passes the data to each function in turn to make a matrix of predictions, and then multiplies that matrix by the vector of weights to get a single, combined vector of predictions.

Usage

# S3 method for caretStack
predict(
  object,
  newdata = NULL,
  se = FALSE,
  level = 0.95,
  excluded_class_id = 0L,
  return_class_only = FALSE,
  verbose = FALSE,
  ...
)

Value

a data.table of predictions

Arguments

object

a caretStack to make predictions from.

newdata

a new dataframe to make predictions on

se

logical, should prediction errors be produced? Default is false.

level

tolerance/confidence level should be returned

excluded_class_id

Which class to exclude from predictions. Note that if the caretStack was trained with an excluded_class_id, that class is ALWAYS excluded from the predictions from the caretList of input models. excluded_class_id for predict.caretStack is for the final ensemble model. So different classes could be excluded from the caretList models and the final ensemble model.

return_class_only

a logical indicating whether to return only the class predictions as a factor. If TRUE, the return will be a factor rather than a data.table. This is a convenience function, and should not be widely used. For example if you have a downstream process that consumes the output of the model, you should have that process consume probabilities for each class. This will make it easier to change prediction probability thresholds if needed in the future.

verbose

a logical indicating whether to print progress

...

arguments to pass to predict.train for the ensemble model. Do not specify type here. For classification, type will always be prob, and for regression, type will always be raw.

Details

Prediction weights are defined as variable importance in the stacked caret model. This is not available for all cases such as where the library model predictions are transformed before being passed to the stacking model.

Examples

models <- caretList(
  x = iris[1:100, 1:2],
  y = iris[1:100, 3],
  methodList = c("rpart", "glm")
)
meta_model <- caretStack(models, method = "lm")
RMSE(predict(meta_model, iris[101:150, 1:2]), iris[101:150, 3])