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From "Alexander Ulanov (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-9120) Add multivariate regression (or prediction) interface
Date Fri, 17 Jul 2015 00:32:04 GMT

    [ https://issues.apache.org/jira/browse/SPARK-9120?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14630596#comment-14630596
] 

Alexander Ulanov commented on SPARK-9120:
-----------------------------------------

I think it should work for the train (aka fit) that has to return the model, not sure about
the model itself. The common ancestor Model does not contain anything that can be called for
prediction, its direct successor PredictionModel has predict:Double. Is there another way
that you were mentioning?

> Add multivariate regression (or prediction) interface
> -----------------------------------------------------
>
>                 Key: SPARK-9120
>                 URL: https://issues.apache.org/jira/browse/SPARK-9120
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 1.4.0
>            Reporter: Alexander Ulanov
>             Fix For: 1.4.0
>
>   Original Estimate: 1h
>  Remaining Estimate: 1h
>
> org.apache.spark.ml.regression.RegressionModel supports prediction only for a single
variable with a method "predict:Double" by extending the Predictor. There is a need for multivariate
prediction, at least for regression. I propose to modify "RegressionModel" interface similarly
to how it is done in "ClassificationModel", which supports multiclass classification. It has
"predict:Double" and "predictRaw:Vector". Analogously, "RegressionModel" should have something
like "predictMultivariate:Vector".



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