``quantile_alpha``
------------------

- Available in: GBM, Deep Learning
- Hyperparameter: yes

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

The ``quantile_alpha`` parameter value defines the desired quantile when performing quantile regression. Used in combination with ``distribution = quantile``, ``quantile_alpha`` activates the quantile loss function. For example, if you want to predict the 80th percentile of the response column’s value, then you can specify ``quantile_alpha=0.8``. The ``quantile_alpha`` value defaults to 0.5 (i.e., the median value, essentially the same as specifying ``distribution=laplace``).

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

- `distribution <distribution.html>`__


Example
~~~~~~~

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

		library(h2o)
		h2o.init()

		# import the boston dataset:
		# this dataset looks at features of the boston suburbs and predicts median housing prices
		# the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Housing
		boston <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")

		# set the predictor names and the response column name
		predictors <- colnames(boston)[1:13]
		# set the response column to "medv", the median value of owner-occupied homes in $1000's
		response <- "medv"

		# convert the chas column to a factor (chas = Charles River dummy variable (= 1 if tract bounds river; 0 otherwise))
		boston["chas"] <- as.factor(boston["chas"])

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

		# try using the `quantile_alpha` parameter:
		# train your model, where you specify distribution = quantile
		# and the quantile_alpha value
		boston_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
		                      validation_frame = valid,
		                      distribution = 'quantile',
		                      quantile_alpha = 0.8,
		                      seed = 1234)

		# print the mse for validation set
		print(h2o.mse(boston_gbm, valid = TRUE))

		# grid over `quantile_alpha` parameter
		# select the values for `quantile_alpha` to grid over
		hyper_params <- list( quantile_alpha = c(0.2, 0.5, 0.8) )

		# this example uses cartesian grid search because the search space is small
		# and we want to see the performance of all models. For a larger search space use
		# random grid search instead: {'strategy': "RandomDiscrete"}

		# build grid search with previously made GBM and hyperparameters
		grid <- h2o.grid(x = predictors, y = response, training_frame = train,
		                 validation_frame = valid, algorithm = "gbm", 
		                 grid_id = "boston_grid", 
		                 distribution = "quantile",
		                 hyper_params = hyper_params,
		                 seed = 1234)

		# Sort the grid models by MSE
		sorted_grid <- h2o.getGrid("boston_grid", sort_by = "mse", decreasing = FALSE)
		sorted_grid

   .. code-tab:: python

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

		# import the boston dataset:
		# this dataset looks at features of the boston suburbs and predicts median housing prices
		# the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Housing
		boston = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")

		# set the predictor names and the response column name
		predictors = boston.columns[:-1]
		# set the response column to "medv", the median value of owner-occupied homes in $1000's
		response = "medv"

		# convert the chas column to a factor (chas = Charles River dummy variable (= 1 if tract bounds river; 0 otherwise))
		boston['chas'] = boston['chas'].asfactor()

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

		# try using the `quantile_alpha` parameter:
		# initialize the estimator then train the model where you specify distribution = quantile
		# and the quantile_alpha value
		boston_gbm = H2OGradientBoostingEstimator(distribution = "quantile", quantile_alpha = .8, seed = 1234)

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

		# print the MSE for the validation data
		print(boston_gbm.mse(valid=True))


		# Example of values to grid over for `quantile_alpha`
		# import Grid Search
		from h2o.grid.grid_search import H2OGridSearch

		# select the values for `quantile_alpha` to grid over
		hyper_params = {'quantile_alpha': [.2, .5, .8]}

		# this example uses cartesian grid search because the search space is small
		# and we want to see the performance of all models. For a larger search space use
		# random grid search instead: {'strategy': "RandomDiscrete"}
		# initialize the GBM estimator
		boston_gbm_2 = H2OGradientBoostingEstimator(distribution="quantile", seed = 1234,
		                                              stopping_metric = "mse", stopping_tolerance = 1e-4)

		# build grid search with previously made GBM and hyper parameters
		grid = H2OGridSearch(model = boston_gbm_2, hyper_params = hyper_params,
		                     search_criteria = {'strategy': "Cartesian"})

		# train using the grid
		grid.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

		# sort the grid models by decreasing MSE
		sorted_grid = grid.get_grid(sort_by = 'mse', decreasing = False)
		print(sorted_grid)