Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6504#discussion_r31369208
--- Diff: docs/ml-guide.md ---
@@ -157,6 +174,49 @@ There are now several algorithms in the Pipelines API
which are not in the lower
* [Feature Extraction, Transformation, and Selection](ml-features.html)
* [Ensembles](ml-ensembles.html)
+## Linear Methods with Elastic Net Regularization
+
+In MLlib, we implement popular linear methods such as logistic regression
and linear least squares with L1 or L2 regularization. Refer to [the linear
methods section](mllib-linear-methods.html) for details. In `spark.ml`, we add
the [Elastic net](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf),
which is a hybrid of L1 and L2 regularization. Mathematically it is defined as
a linear combination of the L1-norm and the L2-norm:
--- End diff --
> In `spark.ml`, we add the [Elastic
net](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf), which is a
hybrid of L1 and L2 regularization.
This makes it sound like it's only in spark.ml. Can this please instead
say that we provide a Pipelines API? The main thing needed here is a code
example, which should demonstrate how to do L1, L2, and a mix. (But I like the
note about how it uses a different optimizer.)
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