Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15394#discussion_r82503487
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala ---
@@ -45,34 +47,48 @@ private[ml] class WeightedLeastSquaresModel(
* 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^,
+ * + lambda / delta (1/2 (1 - alpha) sum,,j,, (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`.
*
* @param fitIntercept whether to fit intercept. If false, z is 0.0.
- * @param regParam L2 regularization parameter (lambda)
+ * @param regParam L2 regularization parameter (lambda).
+ * @param elasticNetParam the ElasticNet mixing parameter.
* @param standardizeFeatures whether to standardize features. If true,
sigma_,,j,, is the
* population standard deviation of the j-th
column of A. Otherwise,
* sigma,,j,, is 1.0.
* @param standardizeLabel whether to standardize label. If true, delta is
the population standard
* deviation of the label column b. Otherwise,
delta is 1.0.
+ * @param solverType the type of solver to use for optimization.
+ * @param maxIter maximum number of iterations when stochastic
optimization is used.
--- End diff --
Clarify it more clearly? only applicable for ```QuasiNewtonSolver```?
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