Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/8197#discussion_r37896794
--- Diff: docs/ml-linear-methods.md ---
@@ -23,20 +23,41 @@ displayTitle: <a href="ml-guide.html">ML</a> - Linear
Methods
\]`
-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 in mllib](mllib-linear-methods.html) for details. In `spark.ml`, we
also include Pipelines API for [Elastic
net](http://en.wikipedia.org/wiki/Elastic_net_regularization), a hybrid of L1
and L2 regularization proposed in [this
paper](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf).
Mathematically it is defined as a linear combination of the L1-norm and the
L2-norm:
+In MLlib, we implement popular linear methods such as logistic
+regression and linear least squares with $L_1$ or $L_2$ regularization.
+Refer to [the linear methods in mllib](mllib-linear-methods.html) for
+details. In `spark.ml`, we also include Pipelines API for [Elastic
+net](http://en.wikipedia.org/wiki/Elastic_net_regularization), a hybrid
+of $L_1$ and $L_2$ regularization proposed in [Zou et al, Regularization
+and variable selection via the elastic
+net](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf).
+Mathematically, it is defined as a convex combination of the $L_1$ and
+the $L_2$ regularization terms:
`\[
-\alpha \|\wv\|_1 + (1-\alpha) \frac{1}{2}\|\wv\|_2^2, \alpha \in [0, 1].
+\alpha~\lambda \|\wv\|_1 + (1-\alpha) \frac{\lambda}{2}\|\wv\|_2^2, \alpha
\in [0, 1], \lambda \geq 0.
--- End diff --
I don't think you need to put lambda in, but if you do, then how about
putting it outside of big parentheses or brackets to make the equation easier
to read?
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