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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