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https://issues.apache.org/jira/browse/SPARK-17789?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Seth Hendrickson updated SPARK-17789:
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Description:
In the initial implementation of initalModel, we allow users to set the initial
model with a KMeansModel that has a different {{k}} than the current model. We
throw an error at train time if the two are mismatched. This means that the
following code throws a runtime exception:
{code}
val kmeansModel = new KMeans().setInitialModel(model).fit(df)
{code}
We should discuss this behavior, and decide if we should enforce users to set
both the initial model and k, or if we should alter k when the initial model is
set, or if we should keep the current behavior.
was:
In the initial implementation of initalModel, we allow users to set the initial
model with a KMeansModel that has a different {{k}} than the current model. We
throw an error at train time if the two are mismatched. This means that the
following code throws a runtime exception:
{{code}}
val kmeansModel = new KMeans().setInitialModel(model).fit(df)
{{code}}
We should discuss this behavior, and decide if we should enforce users to set
both the initial model and k, or if we should alter k when the initial model is
set, or if we should keep the current behavior.
> Don't force users to set k for KMeans if initial model is set
> -------------------------------------------------------------
>
> Key: SPARK-17789
> URL: https://issues.apache.org/jira/browse/SPARK-17789
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Seth Hendrickson
> Priority: Minor
>
> In the initial implementation of initalModel, we allow users to set the
> initial model with a KMeansModel that has a different {{k}} than the current
> model. We throw an error at train time if the two are mismatched. This means
> that the following code throws a runtime exception:
> {code}
> val kmeansModel = new KMeans().setInitialModel(model).fit(df)
> {code}
> We should discuss this behavior, and decide if we should enforce users to set
> both the initial model and k, or if we should alter k when the initial model
> is set, or if we should keep the current behavior.
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