``build_tree_one_node``
-----------------------

- Available in: GBM, DRF, Isolation Forest, XGBoost, Uplift DRF
- Hyperparameter: no

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

Enable this option to specify that the algorithm will run on a single node. This option is suitable for small datasets as there is no network overhead, but fewer CPUs are used. 

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

- None


Example
~~~~~~~

.. tabs::
   .. code-tab:: r R

		library(h2o)
		h2o.init()

		# import the cars dataset:
		# this dataset is used to classify whether or not a car is economical based on
		# the car's displacement, power, weight, and acceleration, and the year it was made
		cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

		# convert response column to a factor
		cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"])

		# set the predictor names and the response column name
		predictors <- c("displacement", "power", "weight", "acceleration", "year")
		response <- "economy_20mpg"

		# split into train and validation sets
		cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234)
		train <- cars_split[[1]]
		valid <- cars_split[[2]]

		# try using the `build_tree_one_node` parameter:
		cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
		                    validation_frame = valid, build_tree_one_node = TRUE, seed = 1234)

		# print the auc for your model
		print(h2o.auc(cars_gbm, valid = TRUE))
	

   .. code-tab:: python

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

		# import the cars dataset:
		# this dataset is used to classify whether or not a car is economical based on
		# the car's displacement, power, weight, and acceleration, and the year it was made
		cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

		# convert response column to a factor
		cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()

		# set the predictor names and the response column name
		predictors = ["displacement","power","weight","acceleration","year"]
		response = "economy_20mpg"

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

		# try turning on the `build_tree_one_node` parameter:
		# initialize your estimator
		cars_gbm = H2OGradientBoostingEstimator(build_tree_one_node = True, seed = 1234)

		# then train your model
		cars_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

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