[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...
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Github user jkbradley commented on the issue: https://github.com/apache/spark/pull/19106 These are fair arguments. I guess it makes sense to throw an exception; that's fine with me. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user srowen commented on the issue: https://github.com/apache/spark/pull/19106 That's my sentiment too, that it is likely patching over another problem. I'd go with Joseph on this, but at least, issue a warning maybe? I also would support throwing an exception to fail clearly and early in this case. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...
Github user sethah commented on the issue: https://github.com/apache/spark/pull/19106 Ok, I guess I'm surprised that someone even noticed this... So, basically, we are changing the behavior of a private function for a specific case which is actually impossible to ever run into. I don't see the need. I know @jkbradley mentioned that it might happen for linear models, but this method is only used for RandomForest and DecisionTree. @srowen mentioned that finding the root cause would be better, and seems to me the root cause is: there is no root cause. Anyway, if we are analyzing the behavior of this method without considering the context from which it's called, then we need to test for not only all zero probabilities but negative ones as well. Otherwise, we're shoring up one edge case without attending to others. If it is not currently possible to call this method with all zero counts, and we don't want it to be possible ever in the future, why don't we just throw an error? I'm also fine with not changing this at all. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user smurching commented on the issue: https://github.com/apache/spark/pull/19106 @sethah I haven't heard of anybody hitting this issue in practice, but it did seem best to ensure that valid probability distributions would be produced regardless of input. There was some discussion of this in the JIRA: https://issues.apache.org/jira/browse/SPARK-21770 --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user sethah commented on the issue: https://github.com/apache/spark/pull/19106 I'm confused how this issue was discovered in the first place. Did someone actually train an RF/DT and receive all zero probabilities? If so, shouldn't there be a unit test that recreates that scenario? Anyway, AFAICT the only way this could happen is if the RandomForest was trained on an empty DataFrame which couldn't happen because it would fail before the train method was called anyway. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user smurching commented on the issue: https://github.com/apache/spark/pull/19106 This looks good to me! @srowen would you be able to give it another look? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...
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[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...
Github user AmplabJenkins commented on the issue: https://github.com/apache/spark/pull/19106 Merged build finished. Test PASSed. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #19106: [SPARK-21770][ML] ProbabilisticClassificationModel fix c...
Github user SparkQA commented on the issue: https://github.com/apache/spark/pull/19106 **[Test build #81336 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/81336/testReport)** for PR 19106 at commit [`a1dc9fc`](https://github.com/apache/spark/commit/a1dc9fc362c079da1b8536383d38e476f53d5ee4). --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org