Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15394#discussion_r84437865
 Diff: mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala 
@@ 44,35 +46,52 @@ private[ml] class WeightedLeastSquaresModel(
* Given weighted observations (w,,i,,, a,,i,,, b,,i,,), we use the following weighted
least squares
* formulation:
*
 * min,,x,z,, 1/2 sum,,i,, w,,i,, (a,,i,,^T^ x + z  b,,i,,)^2^ / sum,,i,, w_i
 * + 1/2 lambda / delta sum,,j,, (sigma,,j,, x,,j,,)^2^,
+ * min,,x,z,, 1/2 sum,,i,, w,,i,, (a,,i,,^T^ x + z  b,,i,,)^2^ / sum,,i,, w,,i,,
+ * + lambda / delta (1/2 (1  alpha) sumj,, (sigma,,j,, x,,j,,)^2^
+ * + alpha sum,,j,, abs(sigma,,j,, x,,j,,)),
*
 * where lambda is the regularization parameter, and delta and sigma,,j,, are controlled
by
 * [[standardizeLabel]] and [[standardizeFeatures]], respectively.
+ * where lambda is the regularization parameter, alpha is the ElasticNet mixing parameter,
+ * and delta and sigma,,j,, are controlled by [[standardizeLabel]] and [[standardizeFeatures]],
+ * respectively.
*
* Set [[regParam]] to 0.0 and turn off both [[standardizeFeatures]] and [[standardizeLabel]]
to
* match R's `lm`.
* Turn on [[standardizeLabel]] to match R's `glmnet`.
*
+ * @note The coefficients and intercept are always trained in the scaled space, but are
returned
+ * on the original scale. [[standardizeFeatures]] and [[standardizeLabel]] can
be used to
+ * control whether regularization is applied in the original space or the scaled
space.
* @param fitIntercept whether to fit intercept. If false, z is 0.0.
 * @param regParam L2 regularization parameter (lambda)
+ * @param regParam Regularization parameter (lambda).
+ * @param elasticNetParam the ElasticNet mixing parameter.
 End diff 
```the ElasticNet mixing parameter (alpha)```

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