Dong Wang created SPARK-1682:
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Summary: LogisticRegressionWithSGD should support svmlight data
and gradient descent w/o sampling
Key: SPARK-1682
URL: https://issues.apache.org/jira/browse/SPARK-1682
Project: Spark
Issue Type: Improvement
Components: MLlib
Affects Versions: 1.0.0
Reporter: Dong Wang
Fix For: 1.0.0
The LogisticRegressionWithSGD example does not expose the following capability
that already exist inside MLlib:
* reading svmlight data
* regularization with l1 and l2
* add intercept
* write model to a file
* read model and generate predictions
The GradientDescent optimizer does sampling before a gradient step. When input
data is already shuffled beforehand, it is possible to scan data and make
gradient descent for each data instance. This could be potentially more
efficient.
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