You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 05:35:37 UTC
[jira] [Updated] (SPARK-5114) Should Evaluator be a PipelineStage
[ https://issues.apache.org/jira/browse/SPARK-5114?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-5114:
--------------------------------
Labels: bulk-closed (was: )
> Should Evaluator be a PipelineStage
> -----------------------------------
>
> Key: SPARK-5114
> URL: https://issues.apache.org/jira/browse/SPARK-5114
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Affects Versions: 1.2.0
> Reporter: Joseph K. Bradley
> Priority: Major
> Labels: bulk-closed
>
> Pipelines can currently contain Estimators and Transformers.
> Question for debate: Should Pipelines be able to contain Evaluators?
> Pros:
> * Schema check: Evaluators take input datasets with particular schema, which should perhaps be checked before running a Pipeline.
> * Intermediate results:
> ** If a Transformer removes a column (which is not done by built-in Transformers currently but might be reasonable in the future), then the user can never evaluate that column. (However, users could keep all columns around.)
> ** If users have to evaluate after running a Pipeline, then each evaluated column may have to be re-materialized.
> Cons:
> * API: Evaluators do not transform datasets. They produce a scalar (or a few values), which makes it hard to say how they fit into a Pipeline or a PipelineModel.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org