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yuhao yang commented on SPARK-20602: ------------------------------------ Combining this with SPARK-20348. Support squared hinge loss (L2 loss) for LinearSVC. And close SPARK-20348 > Adding LBFGS optimizer and Squared_hinge loss for LinearSVC > ----------------------------------------------------------- > > Key: SPARK-20602 > URL: https://issues.apache.org/jira/browse/SPARK-20602 > Project: Spark > Issue Type: Improvement > Components: ML > Affects Versions: 2.2.0 > Reporter: yuhao yang > > Currently LinearSVC in Spark only supports OWLQN as the optimizer ( check > https://issues.apache.org/jira/browse/SPARK-14709). I made comparison between > LBFGS and OWLQN on several public dataset and found LBFGS converges much > faster for LinearSVC in most cases. > The following table presents the number of training iterations and f1 score > of both optimizers until convergence > ||Dataset||LBFGS with hinge||OWLQN with hinge||LBFGS with squared_hinge|| > |news20.binary| 31 (0.99) | 413(0.99) | 185 (0.99) | > |mushroom| 28(1.0) | 170(1.0)| 24(1.0) | > |madelon|143(0.75) | 8129(0.70)| 823(0.74) | > |breast-cancer-scale| 15(1.0) | 16(1.0)| 15 (1.0) | > |phishing | 329(0.94) | 231(0.94) | 67 (0.94) | > |a1a(adult) | 466 (0.87) | 282 (0.87) | 77 (0.86) | > |a7a | 237 (0.84) | 372(0.84) | 69(0.84) | > data source: > https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html > training code: new LinearSVC().setMaxIter(10000).setTol(1e-6) > LBFGS requires less iterations in most cases (except for a1a) and probably is > a better default optimizer. -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org