[jira] [Commented] (SPARK-13568) Create feature transformer to impute missing values

["Nick Pentreath (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-13568?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15177368#comment-15177368 ] 

Nick Pentreath commented on SPARK-13568:
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Ok - the Imputer will need to compute column stats ignoring NaNs, so SPARK-13639 should add that (whether as default behaviour, or an optional argument)

> Create feature transformer to impute missing values
> ---------------------------------------------------
>
>                 Key: SPARK-13568
>                 URL: https://issues.apache.org/jira/browse/SPARK-13568
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML
>            Reporter: Nick Pentreath
>            Priority: Minor
>
> It is quite common to encounter missing values in data sets. It would be useful to implement a {{Transformer}} that can impute missing data points, similar to e.g. {{Imputer}} in [scikit-learn|http://scikit-learn.org/dev/modules/preprocessing.html#imputation-of-missing-values].
> Initially, options for imputation could include {{mean}}, {{median}} and {{most frequent}}, but we could add various other approaches. Where possible existing DataFrame code can be used (e.g. for approximate quantiles etc).



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