[jira] [Comment Edited] (SPARK-17163) Decide on unified multinomial and binary logistic regression interfaces

["Yanbo Liang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-17163?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15434412#comment-15434412 ] 

Yanbo Liang edited comment on SPARK-17163 at 8/24/16 7:49 AM:
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I think it's hard to unify binary and multinomial logistic regression if we do not make any breaking change.
* Like [~sethah] said, we need to find a way to unify the representation of {{coefficients}} and {{intercept}}. I think flatten the matrix into a vector is still compromise, the best representation should be matrix for {{coefficients}} and vector for {{intercept}} even it's a binary classification problem. This will consistent with other ML models such as {{NaiveBayesModel}} which is also support multi-class classification. But this will introduce big breaking change.
* MLOR and LOR return different result for binary classification when regularization is used.
* Current LOR code base provide both {{setThreshold}} and {{setThresholds}} for binary logistic regression and they have some interactions. If we make MLOR and LOR share the old LOR code base, it will also introduce breaking change for these APIs.
* Model store/load compatibility.

I'm more prefer to keep LOR and MLOR in different APIs, but not very strongly hold my opinion if you have better proposal. Thanks!


was (Author: yanboliang):
I think it's hard to unify binary and multinomial logistic regression if we do not make any breaking change.
* Like [~sethah] said, we need to find a way to unify the representation of {{coefficients}} and {{intercept}}. I think flatten the matrix into a vector is still compromise, the best representation should be matrix for {{coefficients}} and vector for {{intercept}} even it's a binary classification problem. This will consistent with other ML models such as {{NaiveBayesModel}} which is also support multi-class classification. 
* MLOR and LOR return different result for binary classification when regularization is used.
* Current LOR code base provide both {{setThreshold}} and {{setThresholds}} for binary logistic regression and they have some interactions. If we make MLOR and LOR share the old LOR code base, it will also introduce breaking change for these APIs.
* Model store/load compatibility.

I'm more prefer to keep LOR and MLOR in different APIs, but not very strongly hold my opinion if you have better proposal. Thanks!

> Decide on unified multinomial and binary logistic regression interfaces
> -----------------------------------------------------------------------
>
>                 Key: SPARK-17163
>                 URL: https://issues.apache.org/jira/browse/SPARK-17163
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML, MLlib
>            Reporter: Seth Hendrickson
>
> Before the 2.1 release, we should finalize the API for logistic regression. After SPARK-7159, we have both LogisticRegression and MultinomialLogisticRegression models. This may be confusing to users and, is a bit superfluous since MLOR can do basically all of what BLOR does. We should decide if it needs to be changed and implement those changes before 2.1



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