From 2a6ac6fd3442a3adba3d70b7625008abbd80d154 Mon Sep 17 00:00:00 2001 From: tqchen Date: Fri, 11 Mar 2016 11:53:24 -0800 Subject: [PATCH] Update JVM Doc --- doc/jvm/xgboost4j-intro.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/doc/jvm/xgboost4j-intro.md b/doc/jvm/xgboost4j-intro.md index c2638cd01..bc8509d79 100644 --- a/doc/jvm/xgboost4j-intro.md +++ b/doc/jvm/xgboost4j-intro.md @@ -38,7 +38,7 @@ XGBoost **does its best in one thing -- tree boosting** and is **being designed We strongly believe that machine learning solution should not be restricted to certain language or certain platform. Specifically, users will be able to use distributed XGBoost in both Flink and Spark. -We have made the API in a portable way so it is also straight-forward to other Dataflow platforms provided by the Cloud. +We have made the API in a portable way so it **can be easily ported to other Dataflow frameworks provided by the Cloud**. XGBoost4J shares its core with other XGBoost libraries, which means data scientists can use R/python read and visualize the model trained distributedly. It also means that user can start with single machine version for exploration, @@ -48,7 +48,6 @@ which already can handle hundreds of million examples. In the following Figure, we describe the overall architecture of XGBoost4J. XGBoost4J provides the Java/Scala API calling the core functionality of XGBoost library. Most importantly, it not only supports the single-machine model training, but also provides an abstraction layer which masks the difference of the underlying data processing engines (they can be Spark, Flink, or just distributed servers across the cluster) - ![XGBoost4J Architecture](https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/xgboost4j.png) @@ -121,7 +120,6 @@ val xgboostModel = XGBoost.train(trainRDD, paramMap, numRound) The next step is to evaluate the model, you can either predict in local side or in a distributed fashion -In Spark ```scala // testSet is an RDD containing testset data represented as