[jira] [Resolved] (SPARK-7085) Inconsistent default miniBatchFraction parameters in the train methods of RidgeRegression

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-7085?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joseph K. Bradley resolved SPARK-7085.
--------------------------------------
       Resolution: Fixed
    Fix Version/s: 1.4.0

Issue resolved by pull request 5658
[https://github.com/apache/spark/pull/5658]

> Inconsistent default miniBatchFraction parameters in the train methods of RidgeRegression
> -----------------------------------------------------------------------------------------
>
>                 Key: SPARK-7085
>                 URL: https://issues.apache.org/jira/browse/SPARK-7085
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 1.3.1
>            Reporter: Nobuyuki Kuromatsu
>            Priority: Minor
>             Fix For: 1.4.0
>
>   Original Estimate: 168h
>  Remaining Estimate: 168h
>
> The miniBatchFraction parameter in the train method called with 4 arguments is 0.01, that is,
> {code:title=RidgeRegression.scala|borderStyle=solid}
> def train(
>       input: RDD[LabeledPoint],
>       numIterations: Int,
>       stepSize: Double,
>       regParam: Double): RidgeRegressionModel = {
>     train(input, numIterations, stepSize, regParam, 0.01)
>   }
> {code}
> but, the parameter is 1.0 in the other train methods. For example,
> {code:title=RidgeRegression.scala|borderStyle=solid}
>   def train(
>       input: RDD[LabeledPoint],
>       numIterations: Int): RidgeRegressionModel = {
>     train(input, numIterations, 1.0, 0.01, 1.0)
>   }
> {code}



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