1. Start the Spark shell:

        $ spark-shell

2. Perform the required imports:

        scala> import org.apache.spark.ml.classification.{GBTClassificationModel,
        GBTClassifier}
scala> import 
        org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

3. Load and parse the data:

        scala> val data = 
        spark.read.format("libsvm").load("s3a://sparkcookbook/patientdata")

4. Split the data into training and test datasets:

        scala> val Array(training, test) = data.randomSplit(Array(0.7, 0.3))

5. Create a classification as a boosting strategy and set the number of iterations to 3:

        scala> val gbt = new GBTClassifier().setMaxIter(10)

6. Train the model:

        scala> val model = gbt.fit(training)

7. Evaluate the model on the test instances and compute the test error:

        scala> val predictions = model.transform(test)
        scala> val evaluator = new 
        MulticlassClassificationEvaluator().setMetricName("accuracy")
        scala> val accuracy = evaluator.evaluate(predictions)

In this case, the accuracy of the model is 75 percent, which is almost the same as what we got for a random forest.