spark-gbtlr (homepage)

Hybrid model of Gradient Boosting Trees and Logistic Regression (GBDT+LR) on Spark

@titicaca / (1)

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: Practical Lessons from Predicting Clicks on Ads at Facebook.

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.


  • 1|ml
  • 1|machine learning

How to

This package doesn't have any releases published in the Spark Packages repo, or with maven coordinates supplied. You may have to build this package from source, or it may simply be a script. To use this Spark Package, please follow the instructions in the README.


Version: v2.4.0 ( 537aa2 | zip ) / Date: 2019-01-02 / License: Apache-2.0