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https://issues.apache.org/jira/browse/SPARK-17094?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15469765#comment-15469765
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Sean Owen commented on SPARK-17094:
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This is already pretty much possible as:
{code}
val model = new Pipeline(new Tokenizer(), new CountVectorizer(),...).fit(data)
{code}
How would you configure the elements of the pipeline?
How would you configure non-linear pipelines?
You're suggesting adding a third type of API. I just don't think this is worth
it given that if you answer the points here it'll be the same as the current
API, just different.
> provide simplified API for ML pipeline
> --------------------------------------
>
> Key: SPARK-17094
> URL: https://issues.apache.org/jira/browse/SPARK-17094
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: yuhao yang
>
> Many machine learning pipeline has the API for easily assembling transformers.
> One example would be:
> val model = new Pipeline("tokenizer", "countvectorizer", "lda").fit(data).
> Appreciate feedback and suggestions.
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