Return-Path: X-Original-To: apmail-spark-issues-archive@minotaur.apache.org Delivered-To: apmail-spark-issues-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 67F9617827 for ; Fri, 3 Apr 2015 20:01:53 +0000 (UTC) Received: (qmail 38955 invoked by uid 500); 3 Apr 2015 20:01:53 -0000 Delivered-To: apmail-spark-issues-archive@spark.apache.org Received: (qmail 38924 invoked by uid 500); 3 Apr 2015 20:01:53 -0000 Mailing-List: contact issues-help@spark.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Delivered-To: mailing list issues@spark.apache.org Received: (qmail 38914 invoked by uid 99); 3 Apr 2015 20:01:53 -0000 Received: from arcas.apache.org (HELO arcas.apache.org) (140.211.11.28) by apache.org (qpsmtpd/0.29) with ESMTP; Fri, 03 Apr 2015 20:01:53 +0000 Date: Fri, 3 Apr 2015 20:01:53 +0000 (UTC) From: "Joseph K. Bradley (JIRA)" To: issues@spark.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Updated] (SPARK-6683) Handling feature scaling properly for GLMs MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 [ https://issues.apache.org/jira/browse/SPARK-6683?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-6683: ------------------------------------- Summary: Handling feature scaling properly for GLMs (was: GLMs with GradientDescent could scale step size instead of features) > Handling feature scaling properly for GLMs > ------------------------------------------ > > Key: SPARK-6683 > URL: https://issues.apache.org/jira/browse/SPARK-6683 > Project: Spark > Issue Type: Improvement > Components: MLlib > Affects Versions: 1.3.0 > Reporter: Joseph K. Bradley > Priority: Minor > > GeneralizedLinearAlgorithm can scale features. This improves optimization behavior (and also affects the optimal solution, as is being discussed and hopefully fixed by [https://github.com/apache/spark/pull/5055]). > This is a bit inefficient since it requires making a rescaled copy of the data. > GradientDescent could instead scale the step size separately for each feature (and adjust regularization as needed; see the PR linked above). This would require storing a vector of length numFeatures, rather than making a full copy of the data. > I haven't thought this through for LBFGS, so I'm not sure if it's generally usable or would require a specialization for GLMs with GradientDescent. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org For additional commands, e-mail: issues-help@spark.apache.org