``training_frame``
------------------

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

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

The ``training_frame`` parameter specifies the dataset that you want to use when you are ready to build a model.

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

- `model_id <model_id.html>`__
- `validation_frame <validation_frame.html>`__


Example
~~~~~~~

.. example-code::
   .. code-block:: 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 `training_frame` parameter:
	# train your model, where you specify your 'x' predictors, your 'y' the response column
	# training_frame and validation_frame
	cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
	                    validation_frame = valid, seed = 1234)

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

   .. code-block:: 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 using the `training_frame` parameter:
	# first initialize your estimator
	cars_gbm = H2OGradientBoostingEstimator(seed = 1234)

	# then train your model, where you specify your 'x' predictors, your 'y' the response column
	# training_frame and validation_frame
	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)