The final thing

As we mentioned earlier, one of the interesting additions to spark 2.0.0 is the ML pipeline. A pipeline is nothing but a linear graph of transformers and estimators. If we look at the classes we have been using, they are either transformers or estimators. We had a decent pipeline for our classification example, as follows:

We started with Passengers, which was the Dataset that we read in.

• Passengers1 was after the feature extraction.
• Passenders2 was after StringIndexer.
• Passengers3 was after the na.drop() function.
• Passengers4 was after the VectorAssembler() function.
• The algTree object was the algorithm object.

We would have created a pipeline:

valtreePipeline = new Pipeline().setStages(Array(indexer, assembler, algTree)) 

Then, we would have created a model:

valmdlTree = treePipeline.fit(trainData) 

Finally, we would have predicted as usual:

val predictions = mdlTree.transform(testData) 

Of course, our original sequence won't work. We have to do na.drop() on passenger1 and...