Github user sethah commented on a diff in the pull request: https://github.com/apache/spark/pull/15394#discussion_r83090919 --- 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). --- End diff -- I changed it to say "Regularization parameter (lambda)". Since lambda is specified above, it should be clear.
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