From d138aa8ee23f4450242da3ac70a493229a90c76b Mon Sep 17 00:00:00 2001 From: Omede Firouz Date: Tue, 7 Apr 2015 23:36:31 -0400 Subject: [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 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 --- .../apache/spark/ml/classification/LogisticRegressionSuite.scala | 9 +++++++++ 1 file changed, 9 insertions(+) (limited to 'mllib/src/test') diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index b3d1bfcfbe..35d8c2e16c 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -46,6 +46,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { assert(lr.getPredictionCol == "prediction") assert(lr.getRawPredictionCol == "rawPrediction") assert(lr.getProbabilityCol == "probability") + assert(lr.getFitIntercept == true) val model = lr.fit(dataset) model.transform(dataset) .select("label", "probability", "prediction", "rawPrediction") @@ -55,6 +56,14 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { assert(model.getPredictionCol == "prediction") assert(model.getRawPredictionCol == "rawPrediction") assert(model.getProbabilityCol == "probability") + assert(model.intercept !== 0.0) + } + + test("logistic regression doesn't fit intercept when fitIntercept is off") { + val lr = new LogisticRegression + lr.setFitIntercept(false) + val model = lr.fit(dataset) + assert(model.intercept === 0.0) } test("logistic regression with setters") { -- cgit v1.2.3