We will explore three common classification models available in Spark: linear models, decision trees, and naive Bayes models. Linear models, while less complex, are relatively easier to scale to very large datasets. Decision tree is a powerful non-linear technique, which can be a little more difficult to scale up (fortunately, ML library takes care of this for us!) and more computationally intensive to train, but delivers leading performance in many situations. The naive Bayes models are more simple, but are easy to train efficiently and parallelize (in fact, they require only one pass over the dataset). They can also give reasonable performance in many cases where appropriate feature engineering is used. A naive Bayes model also provides a good baseline model against which we can measure the performance of other models.
Currently, Spark's ML library supports binary classification...
