[
https://issues.apache.org/jira/browse/SPARK-1536?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Manish Amde updated SPARK-1536:
-------------------------------
Description:
The current decision tree implementation in MLlib only supports binary
classification. This task involves adding multiclass classification support to
the decision tree implementation.
The tasks involves:
- Finding the best strategy for multiclass classification among multiple
options:
-- add multi class support to impurity but it won't work well with the
categorical features since the centriod-based ordering assumptions won't hold
true
-- error-correcting output codes
-- one-vs-all
- Code implementation
- Unit tests
- Functional tests
- Performance tests
- Documentation
was:
The current decision tree implementation in MLlib only supports binary
classification. This task involves adding multiclass classification support to
the decision tree implementation.
The tasks involves:
- Finding the best strategy for multiclass classification among multiple
options:
-- add multi class support to impurity but it won't work well with the
categorical features since the centriod-based ordering assumptions won't hold
true
- error-correcting output codes
- one-vs-all
+ Code implementation
+ Unit tests
+ Functional tests
+ Performance tests
+ Documentation
> Add multiclass classification support to MLlib
> ----------------------------------------------
>
> Key: SPARK-1536
> URL: https://issues.apache.org/jira/browse/SPARK-1536
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 0.9.0
> Reporter: Manish Amde
>
> The current decision tree implementation in MLlib only supports binary
> classification. This task involves adding multiclass classification support
> to the decision tree implementation.
> The tasks involves:
> - Finding the best strategy for multiclass classification among multiple
> options:
> -- add multi class support to impurity but it won't work well with the
> categorical features since the centriod-based ordering assumptions won't hold
> true
> -- error-correcting output codes
> -- one-vs-all
> - Code implementation
> - Unit tests
> - Functional tests
> - Performance tests
> - Documentation
--
This message was sent by Atlassian JIRA
(v6.2#6252)
