``binomial_double_trees``
-------------------------

- Available in: DRF
- Hyperparameter: no

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

When building classification models, this option specifies to build twice as many internal trees as the number of trees (one per class). Enabling this option can lead to higher accuracy but lower speed times, while disabling this can result in faster model building. This option is disabled by default.

Note that ``ntrees=50`` by default, so specifying ``binomial_double_trees=TRUE`` without specifying a number of trees will result in 50 trees and 100 internal trees.

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

- `ntrees <ntrees.html>`__

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_splits <- h2o.splitFrame(data =  cars, ratios = 0.8, seed = 1234)
		train <- cars_splits[[1]]
		valid <- cars_splits[[2]]

		# try using the `binomial_double_trees` (boolean parameter):
		car_drf <- h2o.randomForest(x = predictors, y = response, training_frame = train,
		                   validation_frame = valid,
		                   binomial_double_trees = FALSE, seed = 1234)

		# print the auc and the number of trees built with binomial_double_trees turned off
		# see the difference between the number of trees and number of internal trees
		print(paste('without binomial_double_trees', h2o.auc(car_drf, valid = TRUE), sep = ": "))
		print(car_drf@model$model_summary$number_of_trees)
		print(car_drf@model$model_summary$number_of_internal_trees)
		      

		# try using the `binomial_double_trees` (boolean parameter):
		car_drf_2 <- h2o.randomForest(x = predictors, y = response, training_frame = train,
		                   validation_frame = valid,
		                   binomial_double_trees = TRUE, seed = 1234)

		# print the auc and the number of trees built with binomial_double_trees turned on
		# see the difference between the number of trees and number of internal trees
		print(paste('with binomial_double_trees', h2o.auc(car_drf_2, valid = TRUE), sep = ": "))
		print(car_drf_2@model$model_summary$number_of_trees)
		print(car_drf_2@model$model_summary$number_of_internal_trees)

   .. code-tab:: python

		import h2o
		from h2o.estimators.random_forest import H2ORandomForestEstimator
		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 using the binomial_double_trees (boolean parameter):
		# Initialize and train a DRF
		cars_drf = H2ORandomForestEstimator(binomial_double_trees = False, seed = 1234)
		cars_drf.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

		# print the auc and the number of trees built with binomial_double_trees turned off
		print('without binomial_double_trees:', cars_drf.auc(valid=True))


		# Initialize and train a DRF
		cars_drf_2 = H2ORandomForestEstimator(binomial_double_trees = True, seed = 1234)
		cars_drf_2.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

		# print the auc and the number of trees built with binomial_double_trees turned on
		print('with binomial_double_trees:', cars_drf_2.auc(valid=True))



