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https://issues.apache.org/jira/browse/SPARK-5256?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14514963#comment-14514963
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Joseph K. Bradley commented on SPARK-5256:
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I'd disagree. There are almost always ways to do early stopping, either by
looking at the change in loss or parameters. You can also do early stopping
based on a validation set, but that is outside the realm of optimization and
can be discussed in a different JIRA.
I think the current efforts to support (a) max iterations and (b) convergence
tolerance parameters should suffice for stopping criteria.
> Improving MLlib optimization APIs
> ---------------------------------
>
> Key: SPARK-5256
> URL: https://issues.apache.org/jira/browse/SPARK-5256
> Project: Spark
> Issue Type: Umbrella
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: Joseph K. Bradley
>
> *Goal*: Improve APIs for optimization
> *Motivation*: There have been several disjoint mentions of improving the
> optimization APIs to make them more pluggable, extensible, etc. This JIRA is
> a place to discuss what API changes are necessary for the long term, and to
> provide links to other relevant JIRAs.
> Eventually, I hope this leads to a design doc outlining:
> * current issues
> * requirements such as supporting many types of objective functions,
> optimization algorithms, and parameters to those algorithms
> * ideal API
> * breakdown of smaller JIRAs needed to achieve that API
> I will soon create an initial design doc, and I will try to watch this JIRA
> and include ideas from JIRA comments.
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