One possible straightforward explanation might be your solution(s) might be
stuck in local minima?? And depending on your weights initialization, you
are getting different parameters?
 Maybe have same initial weights for both the runs...
or
I would probably test the execution with synthetic dataset with global
solutions..?



On Wed, Aug 6, 2014 at 7:12 PM, Burak Yavuz <bya...@stanford.edu> wrote:

> Hi,
>
> That is interesting. Would you please share some code on how you are
> setting the regularization type, regularization parameters and running
> Logistic Regression?
>
> Thanks,
> Burak
>
> ----- Original Message -----
> From: "SK" <skrishna...@gmail.com>
> To: u...@spark.incubator.apache.org
> Sent: Wednesday, August 6, 2014 6:18:43 PM
> Subject: Regularization parameters
>
> Hi,
>
> I tried different regularization parameter values with Logistic Regression
> for binary classification of my dataset and would like to understand the
> following results:
>
> regType = L2, regParam = 0.0 , I am getting AUC = 0.80 and accuracy of 80%
> regType = L1, regParam = 0.0 , I am getting AUC = 0.80 and accuracy of 50%
>
> To calculate accuracy I am using 0.5 as threshold. prediction <0.5 is class
> 0, and prediction >= 0.5 is class 1.
>
> regParam = 0.0, implies I am not using any regularization, is that correct?
> If so, it should not matter whether I specify L1 or L2, I should get the
> same results. So why is the accuracy value different?
>
> thanks
>
>
>
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-- 
Mohit

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