Mastering Machine Learning with Spark 2.x

Last model - H2O deep learning

So far we used the Spark MLlib for building different models; however, we can use also H2O algorithms as well. So let's try them!

At first, we are going to transfer our training and testing datasets over to H2O and create a DNN for our binary classification problem. To reiterate, this is made possible because Spark and H2O are sharing the same JVM which facilitates passing Spark RDDs over to H2O hex frames and vice versa.

All the models that we have run up to now have been in MLlib but now we are going to use H2O to build a DNN using the same training and testing sets that we used, which means we need to send this data over to our H2O cloud as follows:

val trainingHF = h2oContext.asH2OFrame(trainingData.toDF, ...