Doing classification using gradient boosted trees

Another ensemble learning algorithm is gradient boosted trees (GBTs). GBTs train one tree at a time, where each new tree improves upon the shortcomings of the previously trained trees.

As GBTs train one tree at a time, they can take longer than random forest.

Getting ready

Let us do GBT on the same patient data and see how the accuracy differs. 

How to do it...

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&gt...