Splitting Datasets into Training/Testing/Validating 
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This example shows how to split a single dataset into two datasets, one used for training and the other used for testing. 

Note that when splitting frames, H2O does not give an exact split. It's designed to be efficient on big data using a probabilistic splitting method rather than an exact split. For example, when specifying a 0.75/0.25 split, H2O will produce a test/train split with an expected value of 0.75/0.25 rather than exactly 0.75/0.25. On small datasets, the sizes of the resulting splits will deviate from the expected value more than on big data, where they will be very close to exact.

.. tabs::
   .. code-tab:: r R
   
		library(h2o)
		h2o.init()
		
		# Import the prostate dataset
		prostate <- h2o.importFile(path = "http://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv")
		print(dim(prostate))
		[1] 380   9 
		
		# Split dataset giving the training dataset 75% of the data
		prostate_split <- h2o.splitFrame(data = prostate, ratios = 0.75)
		print(dim(prostate_split[[1]]))
		[1] 291   9
		print(dim(prostate_split[[2]]))
		[1] 89  9
		
		# Create a training set from the 1st dataset in the split
		prostate_train <- prostate_split[[1]]
		
		# Create a testing set from the 2nd dataset in the split
		prostate_test <- prostate_split[[2]]
		
		# Generate a GLM model using the training dataset. x represesnts the predictor column, and y represents the target index.
		prostate_glm <- h2o.glm(y = "CAPSULE", 
		                        x = c("AGE", "RACE", "PSA", "DCAPS"), 
		                        training_frame = prostate_train, 
		                        family = "binomial", 
		                        nfolds = 10, 
		                        alpha = 0.5)
		
		# Predict using the GLM model and the testing dataset
		pred = h2o.predict(object = prostate_glm, newdata = prostate_test)
		
		# View a summary of the prediction with a probability of TRUE
		summary(pred$p1, exact_quantiles = TRUE)
		p1
		Min.   :0.1560
		1st Qu.:0.2954
		Median :0.3535
		Mean   :0.4111
		3rd Qu.:0.4369
		Max.   :0.9989 

   .. code-tab:: python

		import h2o
		from h2o.estimators.glm import H2OGeneralizedLinearEstimator
		h2o.init()
		
		# Import the prostate dataset
		prostate = "http://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv"
		prostate_df = h2o.import_file(path=prostate)
		
		# Split the data into Train/Test/Validation with Train having 70% and test and validation 15% each
		train,test,valid = prostate_df.split_frame(ratios=[.7, .15])
		
		# Generate a GLM model using the training dataset
		glm_classifier = H2OGeneralizedLinearEstimator(family="binomial", nfolds=10, alpha=0.5)
		glm_classifier.train(y="CAPSULE", x=["AGE", "RACE", "PSA", "DCAPS"], training_frame=train)
		
		# Predict using the GLM model and the testing dataset
		predict = glm_classifier.predict(test)
		
		# View a summary of the prediction
		predict.head()
		  predict        p0        p1
		---------  --------  --------
		        1  0.366189  0.633811
		        1  0.351269  0.648731
		        1  0.69012   0.30988
		        0  0.762335  0.237665
		        1  0.680127  0.319873
		        1  0.687736  0.312264
		        1  0.676753  0.323247
		        1  0.685876  0.314124
		        1  0.707027  0.292973
		        0  0.74706   0.25294
		
		[10 rows x 3 columns]