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https://issues.apache.org/jira/browse/SPARK-9120?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15395434#comment-15395434
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Alexander Ulanov commented on SPARK-9120:
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Thanks for the comment, RegressionModel does not extend that trait indeed. 
However it is designed to handle one output variable, as mentioned in the 
description. This presents it from use in multivariate regression.

> 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
>   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".
> Update: After reading the design docs, adding "predictMultivariate" to 
> RegressionModel does not seem reasonable to me anymore. The issue is as 
> follows. RegressionModel 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 
> (https://issues.apache.org/jira/browse/SPARK-5362). 
> 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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