Multilabel classification model evaluation using Spark 2.0

In this recipe, we explore multilabel classification MultilabelMetrics in Spark 2.0 which should not be mixed up with the previous recipe dealing with multiclass classification MulticlassMetrics. The key to exploring this recipe is to concentrate on evaluation metrics such as Hamming loss, accuracy, f1-measure, and so on, and what they measure.

How to do it...

1. Start a new project in IntelliJ or in an IDE of your choice. Make sure that the necessary JAR files are included.

2. Set up the package location where the program will reside:

package spark.ml.cookbook.chapter4

3. Import the necessary packages for SparkContext to get access to the cluster:

import org.apache.spark.sql.SparkSession
import org.apache.spark.mllib.evaluation.MultilabelMetrics
import org.apache.spark.rdd.RDD

4. Create Spark's configuration and SparkContext:

val spark = SparkSession
.builder
.master("local[*]")
.appName("myMultilabel")
.config("spark.sql.warehouse.dir", ".")
.getOrCreate...