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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-6162) Handle missing values in GBM
Date Sat, 09 Jan 2016 13:01:39 GMT

     [ https://issues.apache.org/jira/browse/SPARK-6162?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Sean Owen resolved SPARK-6162.
------------------------------
    Resolution: Won't Fix

> Handle missing values in GBM
> ----------------------------
>
>                 Key: SPARK-6162
>                 URL: https://issues.apache.org/jira/browse/SPARK-6162
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>    Affects Versions: 1.2.1
>            Reporter: Devesh Parekh
>
> We build a lot of predictive models over data combined from multiple sources, where some
entries may not have all sources of data and so some values are missing in each feature vector.
Another place this might come up is if you have features from slightly heterogeneous items
(or items composed of heterogeneous subcomponents) that share many features in common but
may have extra features for different types, and you don't want to manually train models for
every different type.
> R's GBM library, which is what we are currently using, deals with this type of data nicely
by making "missing" nodes in the decision tree (a surrogate split) for features that can have
missing values. We'd like to do the same with MLLib, but LabeledPoint would need to support
missing values, and GradientBoostedTrees would need to be modified to deal with them.



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