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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