text: extensions = dict(
      extra_params=[('verbose', 'FALSE')],
      required_params=['x', 'y', 'training_frame', 'treatment_column'],
      skip_default_set_params_for=['training_frame', 'ignored_columns', 'response_column', 'max_confusion_matrix_size',
                                   'distribution', 'treatment_column'],
      set_params="""
  if (!missing(distribution)) {
    warning("The only bernoulli distribution is supported for Uplift Random Forest.")
    parms$distribution <- 'bernoulli'
  }
  """,
      set_required_params="""
  parms$training_frame <- training_frame
  args <- .verify_dataxy(training_frame, x, y)
  if (!missing(treatment_column)) {
    parms$treatment_column <- treatment_column
  } else {
    stop("Treatment column is required.")  
  }
  parms$ignored_columns <- args$x_ignore
  parms$response_column <- args$y
  """   
  )
  
  
  
  doc = dict(
      preamble="""
  Build a Uplift Random Forest model
  
  Builds a Uplift Random Forest model on an H2OFrame.
  """,
      params=dict(
          verbose="""
  \code{Logical}. Print scoring history to the console (Metrics per tree). Defaults to FALSE.
  """
      ),
      returns="""
  Creates a \linkS4class{H2OModel} object of the right type.
  """,
      seealso="""
  \code{\link{predict.H2OModel}} for prediction
  """,
      examples=""""""
  )
  