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