LabeledPoint data structure for Spark ML

LabeledPoint is a data structure that has been around since the early days for packaging a feature vector along with a label so it can be used in unsupervised learning algorithms. We demonstrate a short recipe that uses LabeledPoint, the Seq data structure, and DataFrame to run a logistic regression for binary classification of the data.

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.ml.feature.LabeledPoint
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.sql._

4. Create Spark's configuration and SparkContext so we can have access to the cluster:

val spark = SparkSession
.builder
.master("local[*]")
.appName...