Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/5038
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Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/5038#issuecomment-83105312
LGTM. Will wait a short time for more comments.
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Github user yanboliang commented on the pull request:
https://github.com/apache/spark/pull/5038#issuecomment-82787376
@srowen , sorry for the misunderstand. I have change to use string
interpolation and delete ```toDebugString``` which may be useless.
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Github user SparkQA commented on the pull request:
https://github.com/apache/spark/pull/5038#issuecomment-82780195
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Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/5038#issuecomment-82359681
Now this doesn't use string interpolation at all, which is the opposite of
what I suggested. I think it's better to use string interpolation, and use it
consistently. I al
Github user SparkQA commented on the pull request:
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Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/5038#discussion_r26485785
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
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@@ -76,7 +76,17 @@ abstract class GeneralizedLinearModel
Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/5038#discussion_r26485698
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
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@@ -76,7 +76,17 @@ abstract class GeneralizedLinearModel
Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/5038#discussion_r26485545
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
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@@ -163,6 +163,10 @@ class LogisticRegressionModel (
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GitHub user yanboliang opened a pull request:
https://github.com/apache/spark/pull/5038
[SPARK-6291] [MLLIB] GLM toString & toDebugString
GLM toString prints out intercept, numFeatures.
For LogisticRegression and SVM model, toString also prints out numClasses,
threshold.
GLM
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