In this recipe, we will explore the Gradient Boosted Tree (GBT) classification in Spark. The GBT requires more care with hyper-parameters and tries before deciding the final outcome. One must remember that it is completely OK to grow shorter trees if using GBT.
- Start a new project in IntelliJ or in an IDE of your choice. Make sure the necessary JAR files are included.
- Set up the package location where the program will reside:
package spark.ml.cookbook.chapter10
- Import the necessary packages for the Spark context:
import org.apache.spark.mllib.evaluation.MulticlassMetrics import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel import org.apache.spark.rdd.RDD import org.apache.spark.mllib.tree.GradientBoostedTrees import org.apache.spark.mllib.tree.configuration.BoostingStrategy import org.apache...