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https://issues.apache.org/jira/browse/SPARK-13568?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15172423#comment-15172423
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yuhao yang edited comment on SPARK-13568 at 3/3/16 5:48 AM:
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Yes, I'm working on supporting numeric values too.
And I agree about the imputation for vector should check the elements in the
vector. I intends to support the 3 use cases you mentioned.
I'll send a PR after some refine and performance benchmark. Thanks
updated:
create a new jira to discuss how to handle NaN in Statistics
was (Author: yuhaoyan):
Yes, I'm working on supporting numeric values too.
And I agree about the imputation for vector should check the elements in the
vector. I intends to support the 3 use cases you mentioned.
I'll send a PR today or tomorrow after some refine and performance benchmark.
Thanks
> 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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