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https://issues.apache.org/jira/browse/SPARK-22871?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16300972#comment-16300972
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Fangzhou Yang commented on SPARK-22871:
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GBTLRClassifier on Spark is designed and implemented by combining
GradientBoostedTrees and Logistic Regressor in Spark MLlib. Features are
firstly trained and transformed into sparse vectors via GradientBoostedTrees,
and then the generated sparse features will be trained and predicted in
Logistic Regression model.
More details about Spark GBTLR can be found in my github repository:
https://github.com/titicaca/spark-gbtlr
> Add GBT+LR Algorithm in MLlib
> -----------------------------
>
> Key: SPARK-22871
> URL: https://issues.apache.org/jira/browse/SPARK-22871
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 2.2.1
> Reporter: Fangzhou Yang
>
> GBTLRClassifier is a hybrid model of Gradient Boosting Trees and Logistic
> Regression.
> It is quite practical and popular in many data mining competitions. In this
> hybrid model, input features are transformed by means of boosted decision
> trees. The output of each individual tree is treated as a categorical input
> feature to a sparse linear classifer. Boosted decision trees prove to be very
> powerful feature transforms.
> Model details about GBTLR can be found in the following paper:
> <a href="https://dl.acm.org/citation.cfm?id=2648589";>Practical Lessons from
> Predicting Clicks on Ads at Facebook</a>
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