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Posted to issues@spark.apache.org by "Mohamed Baddar (JIRA)" <ji...@apache.org> on 2017/03/21 16:45:42 UTC
[jira] [Commented] (SPARK-7129) Add generic boosting algorithm to
spark.ml
[ https://issues.apache.org/jira/browse/SPARK-7129?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15934864#comment-15934864 ]
Mohamed Baddar commented on SPARK-7129:
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[~josephkb] [~sethah] [~meihuawu] [~mlnick] If now one is working on this. Can I start working on it, I have small experience in contributing with starter tasks in Spark. If no one working on it I would love to start reading the design docs mentioned in comments and start discussing next steps
> Add generic boosting algorithm to spark.ml
> ------------------------------------------
>
> Key: SPARK-7129
> URL: https://issues.apache.org/jira/browse/SPARK-7129
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Joseph K. Bradley
>
> The Pipelines API will make it easier to create a generic Boosting algorithm which can work with any Classifier or Regressor. Creating this feature will require researching the possible variants and extensions of boosting which we may want to support now and/or in the future, and planning an API which will be properly extensible.
> In particular, it will be important to think about supporting:
> * multiple loss functions (for AdaBoost, LogitBoost, gradient boosting, etc.)
> * multiclass variants
> * multilabel variants (which will probably be in a separate class and JIRA)
> * For more esoteric variants, we should consider them but not design too much around them: totally corrective boosting, cascaded models
> Note: This may interact some with the existing tree ensemble methods, but it should be largely separate since the tree ensemble APIs and implementations are specialized for trees.
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