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From DB Tsai <dbt...@stanford.edu>
Subject Re: MLlib-Missing Regularization Parameter and Intercept for Logistic Regression
Date Mon, 16 Jun 2014 18:29:46 GMT
Hi Congrui,

We're working on weighted regularization, so for intercept, you can
just set it as 0. It's also useful when the data is normalized but
want to solve the regularization with original data.

Sincerely,

DB Tsai
-------------------------------------------------------
My Blog: https://www.dbtsai.com
LinkedIn: https://www.linkedin.com/in/dbtsai


On Mon, Jun 16, 2014 at 11:18 AM, Xiangrui Meng <mengxr@gmail.com> wrote:
> Someone is working on weighted regularization. Stay tuned. -Xiangrui
>
> On Mon, Jun 16, 2014 at 9:36 AM, FIXED-TERM Yi Congrui (CR/RTC1.3-NA)
> <fixed-term.Congrui.Yi@us.bosch.com> wrote:
>> Hi Xiangrui,
>>
>> Thank you for the reply! I have tried customizing LogisticRegressionSGD.optimizer
as in the example you mentioned, but the source code reveals that the intercept is also penalized
if one is included, which is usually inappropriate. The developer should fix this problem.
>>
>> Best,
>>
>> Congrui
>>
>> -----Original Message-----
>> From: Xiangrui Meng [mailto:mengxr@gmail.com]
>> Sent: Friday, June 13, 2014 11:50 PM
>> To: user@spark.apache.org
>> Cc: user
>> Subject: Re: MLlib-Missing Regularization Parameter and Intercept for Logistic Regression
>>
>> 1. "examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala"
>> contains example code that shows how to set regParam.
>>
>> 2. A static method with more than 3 parameters becomes hard to
>> remember and hard to maintain. Please use LogistricRegressionWithSGD's
>> default constructor and setters.
>>
>> -Xiangrui

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