[GitHub] spark issue #15149: [SPARK-17057] [ML] ProbabilisticClassifierModels' thresh...

2016-09-28 Thread jkbradley
Github user jkbradley commented on the issue: https://github.com/apache/spark/pull/15149 Thanks all for handling this edge case! Coming late to the discussion...but I like the decisions made here. --- If your project is set up for it, you can reply to this email and have your reply

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2016-09-24 Thread srowen
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2016-09-22 Thread SparkQA
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2016-09-22 Thread SparkQA
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2016-09-22 Thread sethah
Github user sethah commented on the issue: https://github.com/apache/spark/pull/15149 Right now, if a multinomial family is used in LOR, it silently ignores `threshold` regardless. I don't really like that behavior, but perhaps we can focus on it in (and add some tests) in

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2016-09-22 Thread SparkQA
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2016-09-21 Thread MLnick
Github user MLnick commented on the issue: https://github.com/apache/spark/pull/15149 Sure - though actually I think it is perhaps simpler to just allow one 0 in validation for `thresholds` - because we definitely don't want to throw an error only at prediction time once the user has

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2016-09-21 Thread holdenk
Github user holdenk commented on the issue: https://github.com/apache/spark/pull/15149 I think it would also be ok to explicitly fail if we don't want to support that - but fail intentionally. --- If your project is set up for it, you can reply to this email and have your reply

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2016-09-21 Thread MLnick
Github user MLnick commented on the issue: https://github.com/apache/spark/pull/15149 Hmm, yes that would be an inconsistent scenario cos `thresholds` would be used in that case rather than `threshold`. And `threshold` could have been set to `0` => `thresholds = [1, 0]` (or set to

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2016-09-21 Thread holdenk
Github user holdenk commented on the issue: https://github.com/apache/spark/pull/15149 Yah I guess we can consider the case where the user explicitly states multinomial as the family but then only has two classes and uses `setThreshold` rather than `setThresholds` an error state

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2016-09-21 Thread MLnick
Github user MLnick commented on the issue: https://github.com/apache/spark/pull/15149 It does introduce a slight inconsistency because setting `thresholds` to [0, 1] for binary is now not allowed, but setting `threshold` to 0 is fine. Still, I think it's valid to set `threshold` to

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2016-09-21 Thread SparkQA
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2016-09-21 Thread srowen
Github user srowen commented on the issue: https://github.com/apache/spark/pull/15149 Ah, do we need to update that? it looks like `threshold` is separate, and overrides `thresholds`. It's just used as a cutoff for the positive class, so it doesn't have same problem when it's 0. You

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2016-09-21 Thread holdenk
Github user holdenk commented on the issue: https://github.com/apache/spark/pull/15149 Sorry for my late review - was giving a talk yesterday so was focused on that. --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If

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2016-09-21 Thread holdenk
Github user holdenk commented on the issue: https://github.com/apache/spark/pull/15149 This looks really reasonable, the only catch is that the thresholds can be effectively set through `setThreshold` as well as `setThresholds`. So we probably also want to update the range

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2016-09-21 Thread sethah
Github user sethah commented on the issue: https://github.com/apache/spark/pull/15149 One small comment, otherwise LGTM. Thanks! --- 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

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2016-09-21 Thread MLnick
Github user MLnick commented on the issue: https://github.com/apache/spark/pull/15149 LGTM --- 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

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2016-09-20 Thread srowen
Github user srowen commented on the issue: https://github.com/apache/spark/pull/15149 Right now, that limit is only for parity with the randomForest package that this is apparently based on. I agree that it's not clear why these couldn't sum to something more than 1. If they were to

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2016-09-20 Thread sethah
Github user sethah commented on the issue: https://github.com/apache/spark/pull/15149 +1 for not changing the sum requirement. I agree that we need to restrict them to sum to something non-zero and all positive. Thanks for the clarification. --- If your project is set up for it,

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2016-09-20 Thread sethah
Github user sethah commented on the issue: https://github.com/apache/spark/pull/15149 Requiring these thresholds to sum <= 1 seems entirely arbitrary. I don't know why thresholds that sum to `0.347` are any more valid than thresholds that sum to `347`. If these are not meant to

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2016-09-20 Thread MLnick
Github user MLnick commented on the issue: https://github.com/apache/spark/pull/15149 @sethah that is the case for R's randomForest: https://github.com/cran/randomForest/blob/master/R/predict.randomForest.R#L47 --- If your project is set up for it, you can reply to this email and

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2016-09-19 Thread sethah
Github user sethah commented on the issue: https://github.com/apache/spark/pull/15149 Why does the sum need to be less than one? That is not the case for R's randomForest "cutoff" parameter. --- If your project is set up for it, you can reply to this email and have your reply appear

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[GitHub] spark issue #15149: [SPARK-17057] [ML] ProbabilisticClassifierModels' thresh...

2016-09-19 Thread srowen
Github user srowen commented on the issue: https://github.com/apache/spark/pull/15149 CC @MLnick @zhengruifeng --- 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