XGBoost4J Code Examples
=======================

## Java API
* [Basic walkthrough of wrappers](src/main/java/ml/dmlc/xgboost4j/java/example/BasicWalkThrough.java)
* [Customize loss function, and evaluation metric](src/main/java/ml/dmlc/xgboost4j/java/example/CustomObjective.java)
* [Boosting from existing prediction](src/main/java/ml/dmlc/xgboost4j/java/example/BoostFromPrediction.java)
* [Predicting using first n trees](src/main/java/ml/dmlc/xgboost4j/java/example/PredictFirstNtree.java)
* [Generalized Linear Model](src/main/java/ml/dmlc/xgboost4j/java/example/GeneralizedLinearModel.java)
* [Cross validation](src/main/java/ml/dmlc/xgboost4j/java/example/CrossValidation.java)
* [Predicting leaf indices](src/main/java/ml/dmlc/xgboost4j/java/example/PredictLeafIndices.java)
* [External Memory](src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java)
* [Early Stopping](src/main/java/ml/dmlc/xgboost4j/java/example/EarlyStopping.java)

## Scala API

* [Basic walkthrough of wrappers](src/main/scala/ml/dmlc/xgboost4j/scala/example/BasicWalkThrough.scala)
* [Customize loss function, and evaluation metric](src/main/scala/ml/dmlc/xgboost4j/scala/example/CustomObjective.scala)
* [Boosting from existing prediction](src/main/scala/ml/dmlc/xgboost4j/scala/example/BoostFromPrediction.scala)
* [Predicting using first n trees](src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictFirstNTree.scala)
* [Generalized Linear Model](src/main/scala/ml/dmlc/xgboost4j/scala/example/GeneralizedLinearModel.scala)
* [Cross validation](src/main/scala/ml/dmlc/xgboost4j/scala/example/CrossValidation.scala)
* [Predicting leaf indices](src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictLeafIndices.scala)
* [External Memory](src/main/scala/ml/dmlc/xgboost4j/scala/example/ExternalMemory.scala)

## Spark API
* [Distributed Training with Spark](src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkMLlibPipeline.scala)

## Flink API
* [Distributed Training with Flink](src/main/scala/ml/dmlc/xgboost4j/scala/example/flink/DistTrainWithFlink.scala)