Machine Learning with Spark - Second Edition

Training the recommendation model

Once we have extracted these simple features from our raw data, we are ready to proceed with model training; ML takes care of this for us. All we have to do is provide the correctly-parsed input dataset we just created as well as our chosen model parameters.

Split the dataset in to training and testing sets with ratio 80:20, as shown in the following lines of code:

def createALSModel() {   val ratings = FeatureExtraction.getFeatures();   val Array(training, test) = ratings.randomSplit(Array(0.8, 0.2))   println(training.first()) }

You can find the code listing at: https://github.com/ml-resources/spark-ml/blob/branch-ed2/Chapter_05/2.0.0/scala-spark-app/src/main/scala/com/spark/recommendation/ALSModeling.scala ...