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https://issues.apache.org/jira/browse/SPARK-16993?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15417144#comment-15417144
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Dulaj Rajitha commented on SPARK-16993:
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Here is the scenario.
My train data set has : features,and label column
Using that I do train and get a model. (Also I do an evaluation using a split
of the training data.)
Using the above model I need to predict for data set which has only id and
features column.
But when using the second data frame I get the error.
So how we use the same model for different data frame for prediction after
evaluation?
> model.transform without label column in random forest regression
> ----------------------------------------------------------------
>
> Key: SPARK-16993
> URL: https://issues.apache.org/jira/browse/SPARK-16993
> Project: Spark
> Issue Type: Question
> Components: Java API, ML
> Reporter: Dulaj Rajitha
>
> I need to use a separate data set to prediction (Not as show in example's
> training data split).
> But those data do not have the label column. (Since these data are the data
> that needs to be predict the label).
> but model.transform is informing label column is missing.
> org.apache.spark.sql.AnalysisException: cannot resolve 'label' given input
> columns: [id,features,prediction]
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