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https://issues.apache.org/jira/browse/SPARK-9120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Alexander Ulanov updated SPARK-9120:
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Description:
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.
was:
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
is hard-coded to have only one output. It is the same problem that I pointed
out long time ago in MLLib (
> 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".
> 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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