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From "Joseph K. Bradley (JIRA)" <>
Subject [jira] [Updated] (SPARK-9120) Add multivariate regression (or prediction) interface
Date Thu, 13 Aug 2015 01:27:46 GMT


Joseph K. Bradley updated SPARK-9120:
    Target Version/s:   (was: 1.5.0)

> Add multivariate regression (or prediction) interface
> -----------------------------------------------------
>                 Key: SPARK-9120
>                 URL:
>             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
> 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".
> Update: After reading the design docs, adding "predictMultivariate" to RegressionModel
does not seem reasonable to me anymore. The issue is as follows. RegressionModel extends PredictionModel
which has "predict:Double". Its "train" method uses "predict:Double" for prediction, i.e.
PredictionModel (and RegressionModel) is hard-coded to have only one output. There exist a
similar problem in MLLib ( 
> The possible solution for this problem might require to redesign the class hierarchy
or addition of a separate interface that extends model. Though the latter means code duplication.

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