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https://issues.apache.org/jira/browse/SPARK-34080?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Weichen Xu resolved SPARK-34080.
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Fix Version/s: 3.2.0
3.1.1
Resolution: Fixed
Issue resolved by pull request 31160
[https://github.com/apache/spark/pull/31160]
> Add UnivariateFeatureSelector to deprecate existing selectors
> -------------------------------------------------------------
>
> Key: SPARK-34080
> URL: https://issues.apache.org/jira/browse/SPARK-34080
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Affects Versions: 3.2.0, 3.1.1
> Reporter: Xiangrui Meng
> Assignee: Huaxin Gao
> Priority: Critical
> Fix For: 3.1.1, 3.2.0
>
>
> In SPARK-26111, we introduced a few univariate feature selectors, which share
> a common set of params. And they are named after the underlying test, which
> requires users to understand the test to find the matched scenarios. It would
> be nice if we introduce a single class called UnivariateFeatureSelector that
> accepts a selection criterion and a score method (string names). Then we can
> deprecate all other univariate selectors.
> For the params, instead of ask users to provide what score function to use,
> it is more friendly to ask users to specify the feature and label types
> (continuous or categorical) and we set a default score function for each
> combo. We can also detect the types from feature metadata if given. Advanced
> users can overwrite it (if there are multiple score function that is
> compatible with the feature type and label type combo). Example (param names
> are not finalized):
> {code}
> selector = UnivariateFeatureSelector(featureCols=["x", "y", "z"],
> labelCol=["target"], featureType="categorical", labelType="continuous",
> select="bestK", k=100)
> {code}
> cc: [~huaxingao] [~ruifengz] [~weichenxu123]
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