``ignored_columns``
-------------------

- Available in: GBM, DRF, Deep Learning, GLM, PCA, GLRM, Naïve-Bayes, K-Means
- Hyperparameter: no

Description
~~~~~~~~~~~

There may be instances when your dataset includes information that you want to be ignored when building a model. Use the ``ignored_columns`` parameter to specify an array of column names that should be ignored. This is a strict parameter that takes into account the exact string of the column name. So, for example, if your dataset includes one column named **Type** and another column named **Types**, and you specify ``ignored_columns=["type"]``, then the algorithm will only ignore the **Type** column and will not ignore the **Types** column.

Related Parameters
~~~~~~~~~~~~~~~~~~

- `ignore_const_cols <ignore_const_cols.html>`__


Example
~~~~~~~

.. example-code::
   .. code-block:: r

	library(h2o)
	h2o.init()
	# import the airlines dataset:
	# This dataset is used to classify whether a flight will be delayed 'YES' or not "NO"
	# original data can be found at http://www.transtats.bts.gov/
	airlines <-  h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")

	# convert columns to factors
	airlines["Year"] <- as.factor(airlines["Year"])
	airlines["Month"] <- as.factor(airlines["Month"])
	airlines["DayOfWeek"] <- as.factor(airlines["DayOfWeek"])
	airlines["Cancelled"] <- as.factor(airlines["Cancelled"])
	airlines['FlightNum'] <- as.factor(airlines['FlightNum'])

	# set the predictor names and the response column name
	predictors <- colnames(airlines[1:9])
	response <- "IsDepDelayed"

	# split into train and validation
	airlines.splits <- h2o.splitFrame(data =  airlines, ratios = .8, seed = 1234)
	train <- airlines.splits[[1]]
	valid <- airlines.splits[[2]]

	# try using the `ignored_columns` parameter:
	col_list <- c('DepTime','CRSDepTime','ArrTime','CRSArrTime')
	# train your model
	airlines.gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid,
	                        seed = 1234)

	# print the auc for the validation data
	print(h2o.auc(airlines.gbm, valid = TRUE))

   .. code-block:: python

	import h2o
	from h2o.estimators.gbm import H2OGradientBoostingEstimator
	h2o.init()

	# import the airlines dataset:
	# This dataset is used to classify whether a flight will be delayed 'YES' or not "NO"
	# original data can be found at http://www.transtats.bts.gov/
	airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")

	# convert columns to factors
	airlines["Year"]= airlines["Year"].asfactor()
	airlines["Month"]= airlines["Month"].asfactor()
	airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
	airlines["Cancelled"] = airlines["Cancelled"].asfactor()
	airlines['FlightNum'] = airlines['FlightNum'].asfactor()

	# set the predictor names and the response column name
	predictors = airlines.columns[:9]
	response = "IsDepDelayed"

	# split into train and validation sets
	train, valid= airlines.split_frame(ratios = [.8], seed = 1234)

	# try using the `ignored_columns` parameter:
	# create a list of column names to ignore
	col_list = ['DepTime','CRSDepTime','ArrTime','CRSArrTime']

	# initialize the estimator and train the model
	airlines_gbm = H2OGradientBoostingEstimator(ignored_columns = col_list, seed =1234)
	airlines_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

	# print the auc for the validation data
	airlines_gbm.auc(valid=True)