-- Forwarded message --
From: rapelly kartheek
Date: Thu, Nov 27, 2014 at 11:47 AM
Subject: How the sequence of blockManagerId's are constructed in
spark/*/storage/blockManagerMasterActor.getPeers()?
To: u...@spark.apache.org
Hi,
I've been fiddling with spark/*/storage/blockManag
Hi, all
As I understand, with feature scaling the optimization algorithm will
converge faster. Here I have a question about doing scaling multi times. I
know that doing more standard scaling will cause no difference. But if I
want to try MinMax scaling, would it be weird to using standard scaling
Thanks, Brennon. I pushed the change and updated the website.
On Wed, Nov 26, 2014 at 8:17 AM, York, Brennon
wrote:
> A diff you say?! Done and done. If someone with privileges to push to the
> SVN site repo could check it out I think we¹d be good to go. @Sean, thanks
> for the repo URL!
>
> On
Hi Yanbo,
As Xiangrui said, the feature scaling in training step is transparent
to users, and
in theory, with/without feature scaling, the optimization should
converge to the same
solution after transforming to the original space.
In short, we do the training in the scaled space, and get the weig
Hi Yanbo,
We scale the model coefficients back after training. So scaling in
prediction is not necessary.
We had some discussion about this. I'd like to treat feature scaling
as part of the feature transformation, and recommend users to apply
feature scaling before training. It is a cleaner solut
A diff you say?! Done and done. If someone with privileges to push to the
SVN site repo could check it out I think we¹d be good to go. @Sean, thanks
for the repo URL!
On 11/25/14, 2:51 PM, "Sean Owen" wrote:
>For the interested, the SVN repo for the site is viewable at
>http://svn.apache.org/vie
We made some changes in code (it generates 1000 * 1000 elements) and memory
limits up to 100M:
def generate = {
for{
j <- 1 to 10
i <- 1 to 1000
} yield(j, i)
}
~/soft/spark-1.1.0-bin-hadoop2.3/bin/spark-submit --master local
--executor-memory 100M --driver-memory 100M --class Spill -
Hi All,
LogisticRegressionWithLBFGS set useFeatureScaling to true default which can
improve the convergence during optimization.
However, other model training method such as LogisticRegressionWithSGD does
not set useFeatureScaling to true by default and the corresponding set
function is private in
