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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2015/04/23 12:21:39 UTC
[jira] [Assigned] (SPARK-7085) Inconsistent default
miniBatchFraction parameters in the train methods of RidgeRegression
[ https://issues.apache.org/jira/browse/SPARK-7085?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-7085:
-----------------------------------
Assignee: Apache Spark
> 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: Apache Spark
> Priority: Minor
> 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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