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Apache Spark commented on SPARK-2505: ------------------------------------- User 'dbtsai' has created a pull request for this issue: https://github.com/apache/spark/pull/1518 > Weighted Regularizer > -------------------- > > Key: SPARK-2505 > URL: https://issues.apache.org/jira/browse/SPARK-2505 > Project: Spark > Issue Type: New Feature > Components: MLlib > Reporter: DB Tsai > Fix For: 1.1.0 > > > The current implementation of regularization in linear model is using > `Updater`, and this design has couple issues as the following. > 1) It will penalize all the weights including intercept. In machine learning > training process, typically, people don't penalize the intercept. > 2) The `Updater` has the logic of adaptive step size for gradient decent, and > we would like to clean it up by separating the logic of regularization out > from updater to regularizer so in LBFGS optimizer, we don't need the trick > for getting the loss and gradient of objective function. > In this work, a weighted regularizer will be implemented, and users can > exclude the intercept or any weight from regularization by setting that term > with zero weighted penalty. Since the regularizer will return a tuple of loss > and gradient, the adaptive step size logic, and soft thresholding for L1 in > Updater will be moved to SGD optimizer. -- This message was sent by Atlassian JIRA (v6.2#6252)