Earlier, with the spam example on binary classification, we saw how we prepared the data, separated it into training and test data, trained the model, and evaluated it against test data before we finally arrived at the metrics. This series of steps can be abstracted in a simplified manner using Spark's Pipeline API.
In this recipe, we'll take a look at how to use the Pipeline API to solve the same classification problem. Imagine the pipeline to be a factory assembly line where things happen one after another. In our case, we'll pass our raw unprocessed data through various processors before we finally feed the data into the classifier.
In this recipe, we'll classify the same spam/ham dataset (https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection) first using the plain Pipeline, and then using a cross-validator to select the best model for us given a grid of parameters.
Let's summarize the steps:
Importing and...
