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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-9120) Add multivariate regression (or prediction) interface
Date Mon, 28 Sep 2015 08:14:05 GMT

     [ https://issues.apache.org/jira/browse/SPARK-9120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Sean Owen updated SPARK-9120:
-----------------------------
    Fix Version/s:     (was: 1.4.0)

> 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 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 (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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