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From "Yanbo Liang (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-16993) model.transform without label column in random forest regression
Date Tue, 16 Aug 2016 08:07:20 GMT

    [ https://issues.apache.org/jira/browse/SPARK-16993?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15422384#comment-15422384
] 

Yanbo Liang commented on SPARK-16993:
-------------------------------------

[~dulajrajitha] I can not reproduce your reported issue, the following code works well.
{code}
    val data = spark.read.format("libsvm").load("/Users/yliang/data/trunk0/spark/data/mllib/sample_libsvm_data.txt")

    val featureIndexer = new VectorIndexer()
      .setInputCol("features")
      .setOutputCol("indexedFeatures")
      .setMaxCategories(4)
      .fit(data)

    val trainingData = data
    val testData = data.drop("label")

    val rf = new RandomForestRegressor()
      .setLabelCol("label")
      .setFeaturesCol("indexedFeatures")

    val pipeline = new Pipeline()
      .setStages(Array(featureIndexer, rf))

    val model = pipeline.fit(trainingData)

    val predictions = model.transform(testData)

    predictions.select("prediction", "features").show(5)
{code}
Could you tell me whether this code snippet coincide with your issues? If yes, I think it's
not a bug. Thanks!

> 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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