title: Spark MLLib How to ignore features when training a classifier
body: <p>I would like to train a classifier on an RDD[LabeledPoint] using only a subset of the features in each LabeledPoint (both to quickly adjust the model, and to include items in each LabeledPoint such as IDs or evaluation metrics that are not features). I have searched the documentation and cannot find a way to specify which columns should be included or ignored. Code is below, am using Spark and MLLib 1.3.1, Scala 2.10.4.</p>
  
  <p>If specific feature exclusion is not possible, it would still be helpful to include an ID with each data point that is ignored during training. Any help is appreciated!</p>
  
  <pre><code>val numClasses = 2
  val categoricalFeaturesInfo = Map[Int, Int](5 -&gt; 2)
  val numTrees = 100
  val featureSubsetStrategy = "auto"
  val impurity = "gini"
  val maxDepth = 6
  val maxBins = 20
  val model = RandomForest.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)
  </code></pre>