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https://issues.apache.org/jira/browse/SPARK-17163?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15434412#comment-15434412
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Yanbo Liang edited comment on SPARK-17163 at 8/24/16 7:52 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 more or less 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 for 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 more or less 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!
> 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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