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Posted to user@spark.apache.org by Sameer Tilak <ss...@live.com> on 2014/11/03 21:52:54 UTC
Model characterization
Hi All,
I have been using LinearRegression model of MLLib and very pleased with its scalability and robustness. Right now, we are just calculating MSE of our model. We would like to characterize the performance of our model. I was wondering adding support for computing things such as Confidence Interval etc. are they something that are on the roadmap? Graphical things such as ROC curves etc. will that be supported by MLLib/other parts of the ecosystem? or is this something for which other statistical packages are recommended?
Re: Model characterization
Posted by vinay453 <vi...@gmail.com>.
Go it from a friend - println(model.weights) and println(model.intercept).
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Re: Model characterization
Posted by Xiangrui Meng <me...@gmail.com>.
We recently added metrics for regression:
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
and you can use
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
for ROC if it is a binary classification problem.
-Xiangrui
On Mon, Nov 3, 2014 at 12:52 PM, Sameer Tilak <ss...@live.com> wrote:
> Hi All,
>
> I have been using LinearRegression model of MLLib and very pleased with its
> scalability and robustness. Right now, we are just calculating MSE of our
> model. We would like to characterize the performance of our model. I was
> wondering adding support for computing things such as Confidence Interval
> etc. are they something that are on the roadmap? Graphical things such as
> ROC curves etc. will that be supported by MLLib/other parts of the
> ecosystem? or is this something for which other statistical packages are
> recommended?
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