Joseph K. Bradley created SPARK-7129:
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Summary: 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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