Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6504#discussion_r31588861
--- Diff: docs/ml-guide.md ---
@@ -157,6 +174,80 @@ 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 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:
+`\[
+\alpha \|\wv\|_1 + (1-\alpha) \frac{1}{2}\|\wv\|_2^2, \alpha \in [0, 1].
+\]`
+By setting $\alpha$ properly, it contains both L1 and L2 regularization as
special cases. For example, if a [linear
regression](/api/scala/index.html#org.apache.spark.ml.regression.LinearRegression)
model is trained with the elastic net parameter $\alpha$ set to $1$, it is
equivalent to a
[Lasso](http://en.wikipedia.org/wiki/Least_squares#Lasso_method) model. On the
other hand, if $\alpha$ is set to $0$, the trained model reduces to a [ridge
regression](http://en.wikipedia.org/wiki/Tikhonov_regularization) model. We
implement Pipelines API for both linear regression and logistic regression with
elastic net regularization.
+
+**Examples**
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+The following code illustrates how to load a sample dataset and use
logistic regression with elastic net regularization to fit a model.
+
+{% highlight scala %}
+
+import scala.collection.mutable
+import scala.language.reflectiveCalls
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.ml.{Pipeline, PipelineStage}
+import org.apache.spark.ml.classification.{LogisticRegression,
LogisticRegressionModel}
+import org.apache.spark.ml.feature.StringIndexer
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.sql.DataFrame
+
+val regParam = 0.3
--- End diff --
For a simple example, I would not define extra vals here. Just put the
value in the setter method below.
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