ode:
>
> // Discretize data in 16 equal bins since ChiSqSelector requires categorical
> features
> val discretizedData = data.map { lp =>
> LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x => x / 16
> } ) )
> }
>
> I'm sort of missing why "x
sing why "x / 16" is considered a discretization approach
here.
[https://spark.apache.org/docs/latest/mllib-feature-extraction.html#feature-selection]
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ub.com/apache/spark/pull/216 the code and than sbt package?
>
> is it the same as https://github.com/LIDIAgroup/SparkFeatureSelection ???
>
> or something different
>
> filip
>
>
>
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For additional
We have a pending PR (https://github.com/apache/spark/pull/216) for
discretization but it has performance issues. We will try to spend
more time to improve it. -Xiangrui
On Tue, Sep 2, 2014 at 2:56 AM, filipus wrote:
> i guess i found it
>
> https://github.com/LIDIAgroup/SparkFeature
i guess i found it
https://github.com/LIDIAgroup/SparkFeatureSelection
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is there any news about Discretization in spark?
is there anything on git? i didnt find yet
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