Let's apply the decision tree to the diabetes dataset we worked on in the previous recipe:

1. Start the Spark shell or the Databricks Cloud shell and do the necessary imports:

        $ spark-shell
        scala> import 
        org.apache.spark.ml.classification.DecisionTreeClassifier
        scala> import 
        org.apache.spark.ml.evaluation.BinaryClassificationEvaluator

2. Read the diabetes data as a DataFrame:

        scala> val data = 
        spark.read.format("libsvm").option("inferschema","true")
        .load("s3a://sparkcookbook/medicaldata/diabetes.libsvm")

3. Split it into training and test datasets:

        scala> val Array(trainingData, testData) = 
        data.randomSplit(Array(0.7, 0.3))

4. Initialize the decision tree classifier:

        scala> val dt = new DecisionTreeClassifier()

5. Train the model using the training data:

        scala> val model = dt.fit(trainingData)

6. Do predictions on the test dataset:

        scala> val predictions = model.transform(testData)

7. Initialize the evaluator:

        scala> val evaluator = new BinaryClassificationEvaluator()

8. Evaluate the predictions:

        scala> val auroc = evaluator.evaluate(predictions)

9. Print the area under the curve:

        scala> println(s"Area under ROC = $auroc")
        Area under ROC = 0.7624556737588652