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authorOmede Firouz <ofirouz@palantir.com>2015-04-07 23:36:31 -0400
committerJoseph K. Bradley <joseph@databricks.com>2015-04-07 23:36:31 -0400
commitd138aa8ee23f4450242da3ac70a493229a90c76b (patch)
tree059bc1504106aba35d1b6cac5e8d428066f022cd /external
parentc83e03948b184ffb3a9418fecc4d2c26ae33b057 (diff)
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[SPARK-6705][MLLIB] Add fit intercept api to ml logisticregression
I have the fit intercept enabled by default for logistic regression, I wonder what others think here. I understand that it enables allocation by default which is undesirable, but one needs to have a very strong reason for not having an intercept term enabled so it is the safer default from a statistical sense. Explicitly modeling the intercept by adding a column of all 1s does not work. I believe the reason is that since the API for LogisticRegressionWithLBFGS forces column normalization, and a column of all 1s has 0 variance so dividing by 0 kills it. Author: Omede Firouz <ofirouz@palantir.com> Closes #5301 from oefirouz/addIntercept and squashes the following commits: 9f1286b [Omede Firouz] [SPARK-6705][MLLIB] Add fitInterceptTerm to LogisticRegression 1d6bd6f [Omede Firouz] [SPARK-6705][MLLIB] Add a fit intercept term to ML LogisticRegression 9963509 [Omede Firouz] [MLLIB] Add fitIntercept to LogisticRegression 2257fca [Omede Firouz] [MLLIB] Add fitIntercept param to logistic regression 329c1e2 [Omede Firouz] [MLLIB] Add fit intercept term bd9663c [Omede Firouz] [MLLIB] Add fit intercept api to ml logisticregression
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