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https://issues.apache.org/jira/browse/SPARK-11136?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15193916#comment-15193916
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Nick Pentreath commented on SPARK-11136:
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I would say the initial model params should take precedence over defaults
in the general case. The most common use of initial model is to warm start
training given new data. Hence usually the same model params would be
trained (excepting cross-validated pipeline etc).
Of course it can be slightly modified for each algorithm depending on the
details.
The user-defined params should definitely take precedence over the others.
> Warm-start support for ML estimator
> -----------------------------------
>
> Key: SPARK-11136
> URL: https://issues.apache.org/jira/browse/SPARK-11136
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Xusen Yin
> Priority: Minor
>
> The current implementation of Estimator does not support warm-start fitting,
> i.e. estimator.fit(data, params, partialModel). But first we need to add
> warm-start for all ML estimators. This is an umbrella JIRA to add support for
> the warm-start estimator.
> Treat model as a special parameter, passing it through ParamMap. e.g. val
> partialModel: Param[Option[M]] = new Param(...). In the case of model
> existing, we use it to warm-start, else we start the training process from
> the beginning.
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