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https://issues.apache.org/jira/browse/SPARK-13568?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15172328#comment-15172328
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Nick Pentreath commented on SPARK-13568:
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Sure, go ahead. However, taking a quick look at your branch, I think the
approach needs a bit of discussion.
I think the Imputer should handle numeric and/or vector columns. If a vector
column, the idea is not to impute an entire vector when it is null, but rather
the missing (null / NaN) values that may be present in each vector.
I guess if a vector column itself has missing values (i.e. entire vector is
null), then the result would look something like what you have done.
I tend to think that usage within a pipeline is more likely to be imputing
missing values from a set of numeric columns, before applying further
transformations into feature vectors. However, we can potentially support all
three use cases.
> 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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