May 2017
Intermediate to advanced
294 pages
7h 33m
English
Content preview from Apache Spark 2.x Cookbook
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How to do it...
- Start the Spark shell:
$ spark-shell
- Perform the required imports:
scala> import org.apache.spark.mllib.tree.DecisionTree scala> import org.apache.spark.mllib.regression.LabeledPoint scala> import org.apache.spark.mllib.linalg.Vectors scala> import org.apache.spark.mllib.tree.configuration.Algo._ scala> import org.apache.spark.mllib.tree.impurity.Entropy
- Load the file:
scala> val data = sc.textFile("tennis.csv")
- Parse the data and load it into LabeledPoint:
scala> val parsedData = data.map {line => val parts = line.split(',').map(_.toDouble) LabeledPoint(parts(0), Vectors.dense(parts.tail)) }
- Train the algorithm with this data:
scala> val model = DecisionTree.train(parsedData, Classification, Entropy, ...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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