[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[
https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=17148452#comment-17148452
]
zhengruifeng commented on SPARK-3181:
-
I am working on blockify+gemv/gemm for better performance, and found that the
huber regression' s solution varies significantly when blockSize is changed.
Suggested by [~weichenxu123], I tested the stability by [shuffling the input
dataset|https://issues.apache.org/jira/browse/SPARK-32060?focusedCommentId=17148412=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-17148412],
and found that:
the solution of LInearSVC and LinearRegression(SquaredError) looks stable when
input is shuffled; while the solution of LinearRegression(huber) varies
significantly;
I also tested scikit-learn's {{HuberRegressor which also use LBFGSB}} as the
solver, its solutions seem stable. So I guess there maybe something wrong in
{{BreezeLBFGSB}}. Maybe using 'Replace {{\sigma}} to {{\exp(\alpha)' or
'Pseudo-Huber loss' is an alternative.}}
> Add Robust Regression Algorithm with Huber Estimator
>
>
> Key: SPARK-3181
> URL: https://issues.apache.org/jira/browse/SPARK-3181
> Project: Spark
> Issue Type: New Feature
> Components: ML
>Affects Versions: 2.2.0
>Reporter: Fan Jiang
>Assignee: Yanbo Liang
>Priority: Major
> Labels: features
> Fix For: 2.3.0
>
> Original Estimate: 0h
> Remaining Estimate: 0h
>
> Linear least square estimates assume the error has normal distribution and
> can behave badly when the errors are heavy-tailed. In practical we get
> various types of data. We need to include Robust Regression to employ a
> fitting criterion that is not as vulnerable as least square.
> In 1973, Huber introduced M-estimation for regression which stands for
> "maximum likelihood type". The method is resistant to outliers in the
> response variable and has been widely used.
> The new feature for MLlib will contain 3 new files
> /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala
> /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala
> /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala
> and one new class HuberRobustGradient in
> /main/scala/org/apache/spark/mllib/optimization/Gradient.scala
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[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16192217#comment-16192217 ] yuhao yang commented on SPARK-3181: --- Regarding to whether to separate Huber loss an an independent Estimator, I don't see there's an direct conflict. IMO, LinearRegression should act as an all-in-one Estimator that allow user to combine whichever loss function, optimizer and regularization to use. It should targets flexibility and also provides some fundamental infrastructure for regression algorithms. In the meantime, we may also support HuberRegression, RidgeRegression and others in independent Estimator, which is more convenient but with less flexibility (also allow specific parameters). As mentioned by Seth, this would require better code abstraction and plugin interface. Besides loss/prediction/optimizer, we also need to provide infrastructure for model summary and serialization. This should only happen after we can compose Estimator like HuberRegression without noticeable code duplication. > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML >Affects Versions: 2.2.0 >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16182643#comment-16182643 ] Weichen Xu commented on SPARK-3181: --- I also vote to combine them as one estimator, here are my two cents: 1, Regression with Huber loss is one kind of linear regression. It makes sense to switch between different loss functions. 2, To combine them as one estimator should be more visible to users. Users should be easy to try linear regression with different loss function. 3, It will reduce lots of code duplication. thanks! > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML >Affects Versions: 2.2.0 >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16181724#comment-16181724 ] Joseph K. Bradley commented on SPARK-3181: -- Re: [~sethah]'s comment about separating Huber Estimator from regular LinearRegression in the PR: This was my initial reaction too, but I can see both sides: * Technically, robust regression using the Huber loss is Linear Regression. As far as I know (and as far as I can tell from Wikipedia), "Linear Regression" refers to the predictive model, not to the loss model. Using Huber loss instead of squared error does not change the predictive model. * Users should get what they expect when they use an Estimator. The average user would not expect Linear Regression to do fancy Huber loss regression. That said, the default behavior *would* be least squares regression, so it's not a real problem. I don't have strong feelings here, but I'm fine with them being combined. Thinking about the past, it was definitely overkill to separate LinearRegression, RidgeRegression, and Lasso in the old RDD-based API. > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML >Affects Versions: 2.2.0 >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16144042#comment-16144042 ] Apache Spark commented on SPARK-3181: - User 'yanboliang' has created a pull request for this issue: https://github.com/apache/spark/pull/19020 > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML >Affects Versions: 2.2.0 >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.4.14#64029) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16015198#comment-16015198 ] Yanbo Liang commented on SPARK-3181: [~mlnick] Yeah, the Breeze bug has been fixed. I marked this JIRA target for 2.3 and will update my PR soon. Thanks. > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16014026#comment-16014026 ] Nick Pentreath commented on SPARK-3181: --- So the Breeze bug is fixed now right? Will this be revived? > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15948554#comment-15948554 ] Yanbo Liang commented on SPARK-3181: [~josephkb] This is still active, I'm struggling with the Breeze L-BFGS-B bug, wish I could fix it soon. I think we can't catch this in 2.2, so I removed the target version. Thanks. > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15948055#comment-15948055 ] Joseph K. Bradley commented on SPARK-3181: -- Is this still active, and should it be targeted at 2.2? > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.15#6346) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15628380#comment-15628380 ] Yanbo Liang commented on SPARK-3181: [~josephkb] Thanks for retargeting. This task was blocked by a breeze bug, I have some ideas in my mind to fix it. I'll retarget this to 2.2. > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15626703#comment-15626703 ] Joseph K. Bradley commented on SPARK-3181: -- Removing target since 2.1 RC1 is being cut soon. Feel free to retarget. > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[
https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15409629#comment-15409629
]
Yanbo Liang commented on SPARK-3181:
[~dbtsai] Yes, we can have {{HuberAggregator}} in {{LinearRegression}} and
switch different loss function by param, then the {{fit}} function will still
output {{LinearRegressionModel}} which only contains {{coefficients,
intercept}}. If this make sense, I can update my PR according to this line of
thinking. I will wait for your first pass of my current PR and collect your
feedback and comments about the issues I mentioned in the PR description. After
that, I can update the implementation by adding {{HuberAggregator}} in
{{LinearRegression}}. The porting will be very straight forward.
> Add Robust Regression Algorithm with Huber Estimator
>
>
> Key: SPARK-3181
> URL: https://issues.apache.org/jira/browse/SPARK-3181
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib
>Reporter: Fan Jiang
>Assignee: Yanbo Liang
> Labels: features
> Original Estimate: 0h
> Remaining Estimate: 0h
>
> Linear least square estimates assume the error has normal distribution and
> can behave badly when the errors are heavy-tailed. In practical we get
> various types of data. We need to include Robust Regression to employ a
> fitting criterion that is not as vulnerable as least square.
> In 1973, Huber introduced M-estimation for regression which stands for
> "maximum likelihood type". The method is resistant to outliers in the
> response variable and has been widely used.
> The new feature for MLlib will contain 3 new files
> /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala
> /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala
> /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala
> and one new class HuberRobustGradient in
> /main/scala/org/apache/spark/mllib/optimization/Gradient.scala
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[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15408982#comment-15408982 ] DB Tsai commented on SPARK-3181: [~yanboliang] My worry is that with different classes, the visibility may be lower, and will be slightly harder for people to try out. Also, we may need to have some duplication in code for computing the stats of model. Do you think it's possible to have `HuberAggregator` in `LinearRegression`, and we call different version based on param? Thanks. > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML, MLlib >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15390588#comment-15390588 ] Apache Spark commented on SPARK-3181: - User 'yanboliang' has created a pull request for this issue: https://github.com/apache/spark/pull/14326 > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML, MLlib >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[
https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15384138#comment-15384138
]
Yanbo Liang commented on SPARK-3181:
[~dbtsai] Should the {{HuberRegression}} share codebase with
{{LinearRegression}} in ML? I saw you suggested to do that. But I found it's
hard to share codebase between the two regression methods although they
accomplish the same goal. Since the output of {{fit}} will be different:
{{LinearRegressionModel}} and {{HuberRegressionModel}}. The former only
contains {{coefficients, intercept}}, but the latter contains {{coefficients,
intercept, scale/sigma}}. It will also involve save/load compatibility issue if
we combine the two model become one. One trick method is we can drop
{{scale/sigma}} and make the {{fit}} by Huber cost function still output
{{LinearRegressionModel}}, but I don't think it's an appropriate way. So I
would like to implement {{HuberRegression}} in a separate file and looking
forward to hear your thought. Thanks!
> Add Robust Regression Algorithm with Huber Estimator
>
>
> Key: SPARK-3181
> URL: https://issues.apache.org/jira/browse/SPARK-3181
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib
>Reporter: Fan Jiang
>Assignee: Yanbo Liang
> Labels: features
> Original Estimate: 0h
> Remaining Estimate: 0h
>
> Linear least square estimates assume the error has normal distribution and
> can behave badly when the errors are heavy-tailed. In practical we get
> various types of data. We need to include Robust Regression to employ a
> fitting criterion that is not as vulnerable as least square.
> In 1973, Huber introduced M-estimation for regression which stands for
> "maximum likelihood type". The method is resistant to outliers in the
> response variable and has been widely used.
> The new feature for MLlib will contain 3 new files
> /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala
> /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala
> /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala
> and one new class HuberRobustGradient in
> /main/scala/org/apache/spark/mllib/optimization/Gradient.scala
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[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15371715#comment-15371715 ] DB Tsai commented on SPARK-3181: I prefer to option 1) as well. We can extend it to elastic net later when we know how to do OWLQNB. Thanks. > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML, MLlib >Reporter: Fan Jiang >Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[
https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15370871#comment-15370871
]
Yanbo Liang commented on SPARK-3181:
[~dbtsai] [~MechCoder]
There is one problem need to be discussed: The scaling factor {{\sigma}} has to
be estimated as well, and in Eq.(6) {{\sigma}} has to be >= 0. So we can have
two alternatives:
* #1, Use {{\sigma}} directly and L-BFGS-B as the solver. We need to bump up
the dependent breeze version to 0.12 firstly. We can only support L2
regularization as what you did in scikit-learn. However, we can not support L1
or elasticNet regularization, and there is no plan to add OWLQN-B to breeze as
https://github.com/scalanlp/breeze/issues/455 said.
* #2, Replace {{\sigma}} to {{\exp(\alpha)}}, then we can support elasticNet
regularization like what we do in {{LinearRegression}}. But there is no proof
that it will be jointly convex with {{\alpha}} for the huber loss function, we
may fall into the local optimum.
I prefer to opinion #1 which will give the exact solution and looking forward
to hear your thoughts.
> Add Robust Regression Algorithm with Huber Estimator
>
>
> Key: SPARK-3181
> URL: https://issues.apache.org/jira/browse/SPARK-3181
> Project: Spark
> Issue Type: New Feature
> Components: ML, MLlib
>Reporter: Fan Jiang
>Assignee: Yanbo Liang
> Labels: features
> Original Estimate: 0h
> Remaining Estimate: 0h
>
> Linear least square estimates assume the error has normal distribution and
> can behave badly when the errors are heavy-tailed. In practical we get
> various types of data. We need to include Robust Regression to employ a
> fitting criterion that is not as vulnerable as least square.
> In 1973, Huber introduced M-estimation for regression which stands for
> "maximum likelihood type". The method is resistant to outliers in the
> response variable and has been widely used.
> The new feature for MLlib will contain 3 new files
> /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala
> /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala
> /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala
> and one new class HuberRobustGradient in
> /main/scala/org/apache/spark/mllib/optimization/Gradient.scala
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[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15363982#comment-15363982 ] Yanbo Liang commented on SPARK-3181: Yes, I'm working on this and will send a PR soon. I will ping [~dbtsai] and [~MechCoder] for review when the PR available. So I reopen this JIRA and assign it to me. Thanks! > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Fan Jiang >Assignee: Fan Jiang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15362637#comment-15362637 ] DB Tsai commented on SPARK-3181: I had worked with [~MechCoder] to implement huber estimator in python-scikit which had been merged. https://github.com/scikit-learn/scikit-learn/pull/5291 [~yanboliang] showed interest in porting it to Spark. > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Fan Jiang >Assignee: Fan Jiang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14909530#comment-14909530 ] DB Tsai commented on SPARK-3181: Ref: Prof. Art Owen's paper, [A robust hybrid of lasso and ridge regression](http://statweb.stanford.edu/~owen/reports/hhu.pdf ). In MLlib/Breeze, since we don't support L-BFGS-B while the scaling factor in Eq.(6) \sigma has to be >= 0, we can replace it by \exp(\sigma). However, the second derivative of Huber loss is not continuous, this will cause some stability issue since L-BFGS requires it for guaranteed convergence. The workaround I'm going to implement will be [Pseudo-Huber loss function](https://en.wikipedia.org/wiki/Huber_loss#Pseudo-Huber_loss_function) which can be used as a smooth approximation of the Huber loss function, and ensures that derivatives are continuous for all degrees. BTW, in robust regression, the scaling factor \sigma has to be estimated as well, and this value can not be a constant. Imagining that, when the optimization is just started with some initial condition, if the initial guess is not good, then most of the training instances will be treated as outliners. As a result, \epsilon will be large initially, but will be one of the parameters to be minimized. See the details in Prof. Art Owen's paper in section 4. > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Fan Jiang >Assignee: Fan Jiang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14745992#comment-14745992 ] Apache Spark commented on SPARK-3181: - User 'fjiang6' has created a pull request for this issue: https://github.com/apache/spark/pull/2096 > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Fan Jiang >Assignee: Fan Jiang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=14745993#comment-14745993 ] Apache Spark commented on SPARK-3181: - User 'fjiang6' has created a pull request for this issue: https://github.com/apache/spark/pull/7722 > Add Robust Regression Algorithm with Huber Estimator > > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: MLlib >Reporter: Fan Jiang >Assignee: Fan Jiang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14661108#comment-14661108 ] Apache Spark commented on SPARK-3181: - User 'fjiang6' has created a pull request for this issue: https://github.com/apache/spark/pull/8013 Add Robust Regression Algorithm with Huber Estimator Key: SPARK-3181 URL: https://issues.apache.org/jira/browse/SPARK-3181 Project: Spark Issue Type: New Feature Components: MLlib Reporter: Fan Jiang Assignee: Fan Jiang Labels: features Original Estimate: 0h Remaining Estimate: 0h Linear least square estimates assume the error has normal distribution and can behave badly when the errors are heavy-tailed. In practical we get various types of data. We need to include Robust Regression to employ a fitting criterion that is not as vulnerable as least square. In 1973, Huber introduced M-estimation for regression which stands for maximum likelihood type. The method is resistant to outliers in the response variable and has been widely used. The new feature for MLlib will contain 3 new files /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala and one new class HuberRobustGradient in /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14652402#comment-14652402 ] Joseph K. Bradley commented on SPARK-3181: -- It looks like this needs to slip to 1.6. I'll retarget it. Add Robust Regression Algorithm with Huber Estimator Key: SPARK-3181 URL: https://issues.apache.org/jira/browse/SPARK-3181 Project: Spark Issue Type: New Feature Components: MLlib Reporter: Fan Jiang Assignee: Fan Jiang Labels: features Original Estimate: 0h Remaining Estimate: 0h Linear least square estimates assume the error has normal distribution and can behave badly when the errors are heavy-tailed. In practical we get various types of data. We need to include Robust Regression to employ a fitting criterion that is not as vulnerable as least square. In 1973, Huber introduced M-estimation for regression which stands for maximum likelihood type. The method is resistant to outliers in the response variable and has been widely used. The new feature for MLlib will contain 3 new files /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala and one new class HuberRobustGradient in /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14643957#comment-14643957 ] Apache Spark commented on SPARK-3181: - User 'fjiang6' has created a pull request for this issue: https://github.com/apache/spark/pull/7722 Add Robust Regression Algorithm with Huber Estimator Key: SPARK-3181 URL: https://issues.apache.org/jira/browse/SPARK-3181 Project: Spark Issue Type: New Feature Components: MLlib Reporter: Fan Jiang Assignee: Fan Jiang Labels: features Original Estimate: 0h Remaining Estimate: 0h Linear least square estimates assume the error has normal distribution and can behave badly when the errors are heavy-tailed. In practical we get various types of data. We need to include Robust Regression to employ a fitting criterion that is not as vulnerable as least square. In 1973, Huber introduced M-estimation for regression which stands for maximum likelihood type. The method is resistant to outliers in the response variable and has been widely used. The new feature for MLlib will contain 3 new files /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala and one new class HuberRobustGradient in /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14120735#comment-14120735 ] Patrick Wendell commented on SPARK-3181: Hey [~fjiang6] please don't set the fix version on an issue until it is actually merged. Also, in general this field set by the committers and not contributors. Add Robust Regression Algorithm with Huber Estimator Key: SPARK-3181 URL: https://issues.apache.org/jira/browse/SPARK-3181 Project: Spark Issue Type: New Feature Components: MLlib Affects Versions: 1.0.2 Reporter: Fan Jiang Priority: Critical Labels: features Original Estimate: 0h Remaining Estimate: 0h Linear least square estimates assume the error has normal distribution and can behave badly when the errors are heavy-tailed. In practical we get various types of data. We need to include Robust Regression to employ a fitting criterion that is not as vulnerable as least square. In 1973, Huber introduced M-estimation for regression which stands for maximum likelihood type. The method is resistant to outliers in the response variable and has been widely used. The new feature for MLlib will contain 3 new files /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala and one new class HuberRobustGradient in /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14118566#comment-14118566 ] Matthew Farrellee commented on SPARK-3181: -- pls excuse my changes to this issue, i'm not planning to work on it, but cannot appear to remove myself as the assignee. Add Robust Regression Algorithm with Huber Estimator Key: SPARK-3181 URL: https://issues.apache.org/jira/browse/SPARK-3181 Project: Spark Issue Type: New Feature Components: MLlib Affects Versions: 1.0.2 Reporter: Fan Jiang Assignee: Matthew Farrellee Priority: Critical Labels: features Fix For: 1.1.1, 1.2.0 Original Estimate: 0h Remaining Estimate: 0h Linear least square estimates assume the error has normal distribution and can behave badly when the errors are heavy-tailed. In practical we get various types of data. We need to include Robust Regression to employ a fitting criterion that is not as vulnerable as least square. In 1973, Huber introduced M-estimation for regression which stands for maximum likelihood type. The method is resistant to outliers in the response variable and has been widely used. The new feature for MLlib will contain 3 new files /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala and one new class HuberRobustGradient in /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Commented] (SPARK-3181) Add Robust Regression Algorithm with Huber Estimator
[ https://issues.apache.org/jira/browse/SPARK-3181?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14106679#comment-14106679 ] Apache Spark commented on SPARK-3181: - User 'fjiang6' has created a pull request for this issue: https://github.com/apache/spark/pull/2096 Add Robust Regression Algorithm with Huber Estimator Key: SPARK-3181 URL: https://issues.apache.org/jira/browse/SPARK-3181 Project: Spark Issue Type: New Feature Components: MLlib Affects Versions: 1.0.2 Reporter: Fan Jiang Priority: Critical Labels: features Fix For: 1.1.1, 1.2.0 Original Estimate: 0h Remaining Estimate: 0h Linear least square estimates assume the error has normal distribution and can behave badly when the errors are heavy-tailed. In practical we get various types of data. We need to include Robust Regression to employ a fitting criterion that is not as vulnerable as least square. In 1973, Huber introduced M-estimation for regression which stands for maximum likelihood type. The method is resistant to outliers in the response variable and has been widely used. The new feature for MLlib will contain 3 new files /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala and one new class HuberRobustGradient in /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.2#6252) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
