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From Xiangrui Meng <men...@gmail.com>
Subject Re: Regularization parameters
Date Thu, 07 Aug 2014 08:16:24 GMT
Which Spark version are you using? -Xiangrui

On Thu, Aug 7, 2014 at 1:12 AM, SK <skrishna.id@gmail.com> wrote:
> Hi,
>
> I am following the code in
> examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
> For setting the parameters and parsing the command line options, I am just
> reusing that code.Params is defined as follows.
>
> case class Params(
>       input: String = null,
>       numIterations: Int = 100,
>       stepSize: Double = 1.0,
>       algorithm: Algorithm = LR,
>       regType: RegType = L2,
>       regParam: Double = 0.1)
>
> I use the command line option --regType to choose L1 or L2, and --regParam
> to set it to 0.0. The option parser code in the example above parses the
> options and creates the LogisticRegression object. It calls
> setRegParam(regParam) to set the regularization parameter and calls the
> updater to set the regType.
> To run LR, I am again using the code in the example above
> (algorithm.run(training).clearThreshold())
>
> The code in the above example computes AUC.  To compute accuracy of the test
> data classification, I map the class to 0 if prediction < 0.5, else it is
> mapped to class 1. THen I compare the predictions with the corresponding
> labels and the number of matches is given by correctCount.
>
> val accuracy =     correctCount.toDouble / predictionAndLabel.count
>
> thanks
>
>
>
> --
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>
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