old_contents: def update_param(name, param):
      if name == 'stopping_metric':
          param['values'] = ['AUTO', 'anomaly_score']
          return param
      return None  # param untouched
  
  
  extensions = dict(
      required_params=['training_frame', 'x'],
      validate_required_params="",
      set_required_params="""
  parms$training_frame <- training_frame
  if(!missing(x))
    parms$ignored_columns <- .verify_datacols(training_frame, x)$cols_ignore
  """,
  )
  
  doc = dict(
      preamble="""
  Trains an Isolation Forest model
  """,
      params=dict(
          x="""A vector containing the \code{character} names of the predictors in the model."""
      ),
      examples="""
  library(h2o)
  h2o.init()
  
  # Import the cars dataset
  f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv"
  cars <- h2o.importFile(f)
  
  # Set the predictors
  predictors <- c("displacement", "power", "weight", "acceleration", "year")
  
  # Train the IF model
  cars_if <- h2o.isolationForest(x = predictors, training_frame = cars,
                                 seed = 1234, stopping_metric = "anomaly_score",
                                 stopping_rounds = 3, stopping_tolerance = 0.1)
  """
  )
  
new_contents: def update_param(name, param):
      if name == 'validation_response_column':
          param['name'] = None
          return param
      if name == 'stopping_metric':
          param['values'] = ['AUTO', 'anomaly_score']
          return param
      return None  # param untouched
  
  extensions = dict(
      required_params=['training_frame', 'x'],
      validate_required_params="",
      set_required_params="""
  parms$training_frame <- training_frame
  if(!missing(x))
    parms$ignored_columns <- .verify_datacols(training_frame, x)$cols_ignore
  """,
      skip_default_set_params_for=['validation_response_column', 'training_frame', 'ignored_columns'],
  )
  
  doc = dict(
      preamble="""
  Trains an Isolation Forest model
  """,
      params=dict(
          x="""A vector containing the \code{character} names of the predictors in the model."""
      ),
      examples="""
  library(h2o)
  h2o.init()
  
  # Import the cars dataset
  f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv"
  cars <- h2o.importFile(f)
  
  # Set the predictors
  predictors <- c("displacement", "power", "weight", "acceleration", "year")
  
  # Train the IF model
  cars_if <- h2o.isolationForest(x = predictors, training_frame = cars,
                                 seed = 1234, stopping_metric = "anomaly_score",
                                 stopping_rounds = 3, stopping_tolerance = 0.1)
  """
  )
  
subject: Disable validation_response_column in R (only Python supported at first)