Timothy Hunter created SPARK-12212:
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Summary: Clarify the distinction between spark.mllib and spark.ml
Key: SPARK-12212
URL: https://issues.apache.org/jira/browse/SPARK-12212
Project: Spark
Issue Type: Sub-task
Components: Documentation
Affects Versions: 1.5.2
Reporter: Timothy Hunter
There is a confusion in the documentation of MLLib as to what exactly MLlib: is
it the package, or is it the whole effort of ML on spark, and how it differs
from spark.ml? Is MLLib going to be deprecated?
We should do the following:
- refer to the mllib the code package as spark.mllib across all the
documentation. Alternative name is "RDD API of MLlib".
- refer to MLlib the project that encompasses spark.ml + spark.mllib as MLlib
(it should be the default)
- replaces reference to "Pipeline API" by spark.ml or the "Dataframe API of
MLlib". I would deemphasize that this API is for building pipelines. Some users
are lead to believe from the documentation that spark.ml can only be used for
building pipelines and that using a single algorithm can only be done with
spark.mllib.
Most relevant places:
- {{mllib-guide.md}}
- {{mllib-linear-methods.md}}
- {{mllib-dimensionality-reduction.md}}
- {{mllib-pmml-model-export.md}}
- {{mllib-statistics.md}}
In these files, most references to {{MLlib}} are meant to refer to
{{spark.mllib}} instead.
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