April 2017
Intermediate to advanced
532 pages
12h 39m
English
book
Machine Learning with Spark - Second Edition
by Rajdeep Dua, Brian O'Neill, Stephen Boesch, Manpreet Singh Ghotra, Nick Pentreath
Content preview from Machine Learning with Spark - Second Edition
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Estimators
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() ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.