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Niketan Pansare resolved SYSTEMML-1238. --------------------------------------- Resolution: Fixed Fix Version/s: SystemML 0.13 Fixed in the commit https://github.com/apache/incubator-systemml/commit/9d0087cbbd250c9b486923555b450602f816cf19 by setting regularization to 0 (similar to that of scikit-learn). > Python test failing for LinearRegCG > ----------------------------------- > > Key: SYSTEMML-1238 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1238 > Project: SystemML > Issue Type: Bug > Components: Algorithms, APIs > Affects Versions: SystemML 0.13 > Reporter: Imran Younus > Assignee: Niketan Pansare > Fix For: SystemML 0.13 > > Attachments: python_LinearReg_test_spark.1.6.log, > python_LinearReg_test_spark.2.1.log > > > [~deron] discovered that the one of the python test ({{test_mllearn_df.py}}) > with spark 2.1.0 was failing because the test score from linear regression > was very low ({{~ 0.24}}). I did a some investigation and it turns out the > the model parameters computed by the dml script are incorrect. In > systemml.12, the values of betas from linear regression model are > {{\[152.919, 938.237\]}}. This is what we expect from normal equation. (I > also tested this with sklearn). But the values of betas from systemml.13 > (with spark 2.1.0) come out to be {{\[153.146, 458.489\]}}. These are not > correct and therefore the test score is much lower than expected. The data > going into DML script is correct. I printed out the valued of {{X}} and {{Y}} > in dml and I didn't see any issue there. > Attached are the log files for two different tests (systemml0.12 and 0.13) > with explain flag. -- This message was sent by Atlassian JIRA (v6.3.15#6346)