``max_iterations``
------------------

- Available in: GLM, PCA, GLRM, K-Means, CoxPH
- Hyperparameter: yes

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

This option specifies the maximum allowed number of iterations (passes over data) during model training. This value must be between 1 and 1e6, inclusive.

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

- None

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
	cars.splits <- h2o.splitFrame(data = cars, ratios = .8)
	train <- cars.splits[[1]]
	valid <- cars.splits[[2]]

	# try using the `max_iterations` parameter:
	car_glm <- h2o.glm(x = predictors, y = response, family = 'binomial', training_frame = train, validation_frame = valid,
	                   max_iterations = 50)

	# print the auc for your validation data
	print(h2o.auc(car_glm, valid = TRUE))

   .. code-block:: python

	import h2o
	from h2o.estimators.glm import H2OGeneralizedLinearEstimator
	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])

	# try using the `max_iterations` parameter:
	# Initialize and train a GLM
	cars_glm = H2OGeneralizedLinearEstimator(family = 'binomial', max_iterations = 50)
	cars_glm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

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