An estimator is an abstraction of a learning algorithm that fits a model on a dataset.
An estimator implements a fit() method that takes a DataFrame and produces a model. An example of a learning algorithm is LogisticRegression.
In a nutshell, the estimator is: DataFrame =[fit]=> Model.
In the following example, PipelineComponentExample introduces the concepts of transformers and estimators:
import org.apache.spark.ml.classification.LogisticRegression import org.apache.spark.ml.linalg.{Vector, Vectors} import org.apache.spark.ml.param.ParamMap import org.apache.spark.sql.Row import org.utils.StandaloneSpark object PipelineComponentExample { def main(args: Array[String]): Unit = { val spark = StandaloneSpark.getSparkInstance() ...
