Random Forrest Classifier in Spark ML

MLlib is Spark’s machine learning (ML) library. It’s goal is to make practical machine learning scalable and easy.

I tried to make a complete step by step classification example using the Iris flower data set  using the BeakerX Jupyter kernel which covers the following steps

• Setup
• Data Preparation
• Testing and Prediction
• Validation

The example is written in Scala but you could use any other language which is supported by the JVM.

My example can be found the this GIST

I leave it up to you to replace the classifier with e.g. NaiveBayes or with a MultilayerPerceptronClassifier. The MLib Programming Guide contains the right level of information and is easy to use.