Bui, Tri
Cc: user@spark.apache.org
Subject: Re: Do I need to applied feature scaling via StandardScaler for LBFGS
for Linear Regression?
You can do something like the following.
val rddVector = input.map({
case (response, vec) => {
val newVec = MLUtils.appendBias(vec)
newVec.to
14 1:26 PM
> To: Bui, Tri
> Cc: user@spark.apache.org
> Subject: Re: Do I need to applied feature scaling via StandardScaler for
> LBFGS for Linear Regression?
>
> It seems that your response is not scaled which will cause issue in LBFGS.
> Typically, people train Linear Regres
---
> From: dbt...@dbtsai.com [mailto:dbt...@dbtsai.com]
> Sent: Friday, December 12, 2014 12:16 PM
> To: Bui, Tri
> Cc: user@spark.apache.org
> Subject: Re: Do I need to applied feature scaling via StandardScaler for
> LBFGS for Linear Regression?
>
> You need to do the Standa
or org.apache.spark.mllib.linalg.Vector should have an "s" to
> match import library org.apache.spark.mllib.linalg.Vectors
>
> Thanks
> Tri
>
>
>
>
>
> -Original Message-
> From: dbt...@dbtsai.com [mailto:dbt...@dbtsai.com]
> Sent: Friday,
linalg.Vectors
Thanks
Tri
-Original Message-
From: dbt...@dbtsai.com [mailto:dbt...@dbtsai.com]
Sent: Friday, December 12, 2014 12:16 PM
To: Bui, Tri
Cc: user@spark.apache.org
Subject: Re: Do I need to applied feature scaling via StandardScaler for LBFGS
for Linear Regression?
You need to do the
You need to do the StandardScaler to help the convergency yourself.
LBFGS just takes whatever objective function you provide without doing
any scaling. I will like to provide LinearRegressionWithLBFGS which
does the scaling internally in the nearly feature.
Sincerely,
DB Tsai
Hi,
Trying to use LBFGS as the optimizer, do I need to implement feature scaling
via StandardScaler or does LBFGS do it by default?
Following code generated error " Failure again! Giving up and returning,
Maybe the objective is just poorly behaved ?".
val data = sc.textFile("file:///data/Tra
