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https://issues.apache.org/jira/browse/SPARK-7085?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joseph K. Bradley updated SPARK-7085:
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Target Version/s: 1.4.0
> 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
> Assignee: 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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