[ 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)