Running SVM with H2O over Spark

In this recipe, we'll see how to run SVM to predict or classify a cancer.

Getting ready

To step through this recipe, you will need a running Spark Cluster in any one of the following modes: Local, standalone, YARN, Mesos. Include the Spark MLlib package in the build.sbt file so that it downloads the related libraries and the API can be used. Install Hadoop (optionally), Scala, and Java. Also, install Sparkling Water as discussed in the preceding recipe.

How to do it…

Please download the dataset from https://github.com/ChitturiPadma/datasets/blob/master/Breast_CancerData.csv. While including the dependencies sparkling-water-core and sparkling-water-ml, please change the version to 1.6.8.

The sample records in the data (with a few columns) look as follows:

Here, the last column label indicates whether the person has breast cancer (represented by B).

The code that runs SVM on the data is as follows:

  import java.io._ 
  import org.apache.spark.ml.spark.models.svm...