[ https://issues.apache.org/jira/browse/SPARK-9120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-9120. --------------------------------- Resolution: Incomplete > 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 > Priority: Major > Labels: bulk-closed > 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. -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org