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...with intercept with L1 regularization ## What changes were proposed in this pull request? In the test, "multinomial logistic regression with intercept with L1 regularization" in the "LogisticRegressionSuite", taking more than a minute due to training of 2 logistic regression model. However after analysing the training cost over iteration, we can reduce the computation time by 50%. Training cost vs iteration for model 1  So, model1 is converging after iteration 150. Training cost vs iteration for model 2  After around 100 iteration, model2 is converging. So, if we give maximum iteration for model1 and model2 as 175 and 125 respectively, we can reduce the computation time by half. ## How was this patch tested? Computation time in local setup : Before change: ~53 sec After change: ~26 sec Please review http://spark.apache.org/contributing.html before opening a pull request. Closes #22659 from shahidki31/SPARK-25623. Authored-by: Shahid <shahidki31@gmail.com> Signed-off-by: Sean Owen <sean.owen@databricks.com> |
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