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From Masood Krohy <masood.kr...@intact.net>
Subject Re: LinearRegressionWithSGD and Rank Features By Importance
Date Mon, 07 Nov 2016 16:27:09 GMT
Yes, you would want to scale those features before feeding into any 
algorithm, one typical way would be to calculate the average and std for 
each feature, deduct the avg, then divide by std. Dividing by "max - min" 
is also a good option if you're sure there is no outlier shooting up your 
max or lowering your min significantly for each feature. After you have 
scaled each feature, then you can feed the data into the algo for 
training. 

For prediction on new samples, you need to scale each sample first before 
making predictions using your trained model. 

It's not too complicated to implement manually, but Spark API has some 
support for this already:
ML: http://spark.apache.org/docs/latest/ml-features.html#standardscaler
MLlib: 
http://spark.apache.org/docs/latest/mllib-feature-extraction.html#standardscaler
Masood


------------------------------
Masood Krohy, Ph.D. 
Data Scientist, Intact Lab-R&D 
Intact Financial Corporation 
http://ca.linkedin.com/in/masoodkh 



De :    Carlo.Allocca <carlo.allocca@open.ac.uk>
A :     Masood Krohy <masood.krohy@intact.net>
Cc :    Carlo.Allocca <carlo.allocca@open.ac.uk>, Mohit Jaggi 
<mohitjaggi@gmail.com>, "user@spark.apache.org" <user@spark.apache.org>
Date :  2016-11-07 10:50
Objet : Re: LinearRegressionWithSGD and Rank Features By Importance



Hi Masood, 

thank you very much for the reply. It is very a good point as I am getting 
very bed result so far. 

If I understood well what you suggest is to scale the date below (it is 
part of my dataset) before applying linear regression SGD.

is it correct?

Many Thanks in advance. 

Best Regards,
Carlo 



On 7 Nov 2016, at 15:31, Masood Krohy <masood.krohy@intact.net> wrote:

If you go down this route (look at actual coefficients/weights), then make 
sure your features are scaled first and have more or less the same mean 
when feeding them into the algo. If not, then actual coefficients/weights 
wouldn't tell you much. In any case, SGD performs badly with unscaled 
features, so you gain if you scale the features beforehand. 
Masood 

------------------------------
Masood Krohy, Ph.D. 
Data Scientist, Intact Lab-R&D 
Intact Financial Corporation 
http://ca.linkedin.com/in/masoodkh 



De :        Carlo.Allocca <carlo.allocca@open.ac.uk> 
A :        Mohit Jaggi <mohitjaggi@gmail.com> 
Cc :        Carlo.Allocca <carlo.allocca@open.ac.uk>, "
user@spark.apache.org" <user@spark.apache.org> 
Date :        2016-11-04 03:39 
Objet :        Re: LinearRegressionWithSGD and Rank Features By Importance 




Hi Mohit, 

Thank you for your reply. 
OK. it means coefficient with high score are more important that other 
with low score…

Many Thanks,
Best Regards,
Carlo


> On 3 Nov 2016, at 20:41, Mohit Jaggi <mohitjaggi@gmail.com> wrote:
> 
> For linear regression, it should be fairly easy. Just sort the 
co-efficients :)
> 
> Mohit Jaggi
> Founder,
> Data Orchard LLC
> www.dataorchardllc.com
> 
> 
> 
> 
>> On Nov 3, 2016, at 3:35 AM, Carlo.Allocca <carlo.allocca@open.ac.uk> 
wrote:
>> 
>> Hi All,
>> 
>> I am using SPARK and in particular the MLib library.
>> 
>> import org.apache.spark.mllib.regression.LabeledPoint;
>> import org.apache.spark.mllib.regression.LinearRegressionModel;
>> import org.apache.spark.mllib.regression.LinearRegressionWithSGD;
>> 
>> For my problem I am using the LinearRegressionWithSGD and I would like 
to perform a “Rank Features By Importance”.
>> 
>> I checked the documentation and it seems that does not provide such 
methods.
>> 
>> Am I missing anything?  Please, could you provide any help on this?
>> Should I change the approach?
>> 
>> Many Thanks in advance,
>> 
>> Best Regards,
>> Carlo
>> 
>> 
>> -- The Open University is incorporated by Royal Charter (RC 000391), an 
exempt charity in England & Wales and a charity registered in Scotland (SC 
038302). The Open University is authorised and regulated by the Financial 
Conduct Authority.
>> 
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>> 
> 


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