Mastering Machine Learning with Spark 2.x

Labeled point vector

Prior to running any supervised machine learning algorithm using Spark MLlib, we must convert our dataset into a labeled point vector which maps features to a given label/response; labels are stored as doubles which facilitates their use for both classification and regression tasks. For all binary classification problems, labels should be stored as either 0 or 1, which we confirmed from the preceding summary statistics holds true for our example.

val higgs = response.zip(features).map {  
case (response, features) =>  
LabeledPoint(response, features) } 
 
higgs.setName("higgs").cache() 

An example of a labeled point vector follows:

(1.0, [0.123, 0.456, 0.567, 0.678, ..., 0.789]) 

In the preceding example, all doubles inside ...