[jira] [Created] (SPARK-7685) Handle high imbalanced data or apply weights to different samples in Logistic Regression

["DB Tsai (JIRA)" <ji...@apache.org>]

DB Tsai created SPARK-7685:
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             Summary: Handle high imbalanced data or apply weights to different samples in Logistic Regression
                 Key: SPARK-7685
                 URL: https://issues.apache.org/jira/browse/SPARK-7685
             Project: Spark
          Issue Type: New Feature
          Components: ML
            Reporter: DB Tsai


In fraud detection dataset, almost all the samples are negative while only couple of them are positive. This type of high imbalanced data will bias the models toward negative resulting poor performance. In python-scikit, they provide a correction allowing users to Over-/undersample the samples of each class according to the given weights. In auto mode, selects weights inversely proportional to class frequencies in the training set. This can be done in a more efficient way by multiplying the weights into loss and gradient instead of doing actual over/undersampling in the training dataset which is very expensive.

http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

On the other hand, some of the training data maybe more important like the training samples from tenure users while the training samples from new users maybe less important. We should be able to provide another "weight: Double" information in the LabeledPoint to weight them differently in the learning algorithm. 



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