May 2017
Intermediate to advanced
294 pages
7h 33m
English
Content preview from Apache Spark 2.x Cookbook
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Doing ridge regression
An alternate way to improve prediction quality is to do ridge regression. In lasso, a lot of the features get their coefficients set to zero and, therefore, eliminated from the equation. In ridge, predictors or features are penalized, but never set to zero. How to do it...
- Start the Spark shell:
$ spark-shell
- Import the statistics and related classes:
scala> import org.apache.spark.ml.linalg.Vectorsscala> import org.apache.spark.ml.regression.LinearRegression
- Create the dataset with the value we created earlier:
scala> val points = spark.createDataFrame(Seq( (1d,Vectors.dense(5,3,1,2,1,3,2,2,1)), (2d,Vectors.dense(9,8,8,9,7,9,8,7,9)))).toDF("label","features")
- Initialize the linear regression estimator ...
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