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https://issues.apache.org/jira/browse/SPARK-3147?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-3147:
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Description: -A/B- Online testing is widely used to compare online models.
We can implement A/B testing in MLlib and integrate it with Spark Streaming.
For example, we have a PairDStream[String, Double], whose keys are model ids
and values are observations (click or not, or revenue associated with the
event). With A/B testing, we can tell whether one model is significantly better
than another at a certain time. There are some caveats. For example, we should
avoid multiple testing and support A/A testing as a sanity check. (was: A/B
testing is widely used to compare online models. We can implement A/B testing
in MLlib and integrate it with Spark Streaming. For example, we have a
PairDStream[String, Double], whose keys are model ids and values are
observations (click or not, or revenue associated with the event). With A/B
testing, we can tell whether one model is significantly better than another at
a certain time. There are some caveats. For example, we should avoid multiple
testing and support A/A testing as a sanity check. )
> Implement streaming testing
> ---------------------------
>
> Key: SPARK-3147
> URL: https://issues.apache.org/jira/browse/SPARK-3147
> Project: Spark
> Issue Type: New Feature
> Components: MLlib, Streaming
> Reporter: Xiangrui Meng
> Assignee: Feynman Liang
> Fix For: 1.6.0
>
>
> -A/B- Online testing is widely used to compare online models. We can
> implement A/B testing in MLlib and integrate it with Spark Streaming. For
> example, we have a PairDStream[String, Double], whose keys are model ids and
> values are observations (click or not, or revenue associated with the event).
> With A/B testing, we can tell whether one model is significantly better than
> another at a certain time. There are some caveats. For example, we should
> avoid multiple testing and support A/A testing as a sanity check.
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