zhengruifeng created SPARK-13712:
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Summary: Add OneVsOne to ML
Key: SPARK-13712
URL: https://issues.apache.org/jira/browse/SPARK-13712
Project: Spark
Issue Type: New Feature
Components: ML
Reporter: zhengruifeng
Priority: Minor
Another Meta method for multi-class classification.
Most classification algorithms were designed for balanced data.
The OneVsRest method will generate K models on imbalanced data.
The OneVsOne will train K*(K-1)/2 models on balanced data.
OneVsOne is less sensitive to the problems of imbalanced datasets, and can
usually result in higher precision.
But it is much more computationally expensive, although each model are trained
on a much smaller dataset. (2/K of total)
The OneVsOne is implemented in the way OneVsRest did:
val classifier = new LogisticRegression()
val ovo = new OneVsOne()
ovo.setClassifier(classifier)
val ovoModel = ovo.fit(data)
val predictions = ovoModel.transform(data)
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