[jira] [Resolved] (SPARK-29327) Support specifying features via multiple columns in Predictor and PredictionModel

["Sean R. Owen (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-29327?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean R. Owen resolved SPARK-29327.
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    Resolution: Won't Fix

> Support specifying features via multiple columns in Predictor and PredictionModel
> ---------------------------------------------------------------------------------
>
>                 Key: SPARK-29327
>                 URL: https://issues.apache.org/jira/browse/SPARK-29327
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, MLlib
>    Affects Versions: 3.0.0
>            Reporter: Liangcai Li
>            Priority: Major
>              Labels: pull-request-available
>
> There are always more features than one in a classification/regression task, however the current API to specify features columns in Predictor of Spark MLLib only supports one single column, which requires users to assemble the multiple features columns into a "org.apache.spark.ml.linalg.Vector" before fitting to Spark ML pipeline. 
> This improvement is going to let users specify the features columns directly without vectorization. To support this, we can introduce two new APIs in both "Predictor" and "PredictionModel", and a new parameter named "featuresCols" storing the features columns names as an Array. ( PR is ready here [https://github.com/apache/spark/pull/25983])
> *APIs:*
> {{def setFeaturesCol(value: Array[String]): M = ...}}
> {{protected def isSupportMultiColumnsForFeatures: Boolean = false}}
> *Parameter:*
> {{final val featuresCols: StringArrayParam = new StringArrayParam(this, "featuresCols",   ...)}}
> Then ML implementations can get and use the features columns names from this new parameter "featuresCols", along with the raw data of features in separate columns directly in dataset.



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