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https://issues.apache.org/jira/browse/SPARK-18205?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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nirav patel updated SPARK-18205:
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Description:
I see that most of the Algorithms are missing documentation. It's really hard
to know what exactly Fit and Transform methods does for given algorithm without
it. e.g. One can assume that IDF model Fit method will compute TF-IDF for given
dataset. but then how it uses that information to Transform new dataset? In
other words, what parameters exactly Estimator learned during Fitting and will
re-use it for Transformation.
Same thing apply for all Estimators and Transformers.
was:
I see that most of the Algorithms are missing documentation. It's really hard
to know what exactly Fit and Transform methods does for given algorithm without
it. e.g. One can assume that IDF model Fit method will compute TF-IDF for given
dataset. but then how it uses that information to Transform new dataset?
Same thing apply for all Estimators and Transformers.
> Spark ML Estimators and Models needs documentation
> ---------------------------------------------------
>
> Key: SPARK-18205
> URL: https://issues.apache.org/jira/browse/SPARK-18205
> Project: Spark
> Issue Type: Bug
> Components: Documentation
> Affects Versions: 2.0.3
> Reporter: nirav patel
>
> I see that most of the Algorithms are missing documentation. It's really hard
> to know what exactly Fit and Transform methods does for given algorithm
> without it. e.g. One can assume that IDF model Fit method will compute TF-IDF
> for given dataset. but then how it uses that information to Transform new
> dataset? In other words, what parameters exactly Estimator learned during
> Fitting and will re-use it for Transformation.
> Same thing apply for all Estimators and Transformers.
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