title: How does spark ML decision tree handle continuous features for regression problems
body: <p>I have a mixture of categorical and continuous features. I have indexed all my categorical variables and I use VectorAssembler to create a features column </p>
  
  <pre><code>        StringIndexerModel indexer = new StringIndexer()
                      .setInputCol("categorical")
                      .setOutputCol("categoricalIdx1")
                      .setHandleInvalid("skip").fit(data);
          VectorAssembler assembler = new VectorAssembler()
                      .setInputCols(new String[]{"categoricalIdx1","continuous"})
                      .setOutputCol("features");
  
          DecisionTreeRegressor dt = new DecisionTreeRegressor()
                                         .setMaxBins(40)
                                         .setMaxDepth(10)
                                         .setFeaturesCol("features")
                                         .setLabelCol("commission")
                                         .setPredictionCol("prediction");
  </code></pre>
  
  <p>I couldn't find any way to specify which features are categorical and which are continuous. And since all the columns have been converted to numerical values how does the DecisionTreeRegressor know the difference. What am I missing here?
  The code seems to work and gives quite good result, But I have a hunch that I am doing something wrong here.</p>