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Cross reference jpmml-sparkml-lightgbm plugin

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terrytangyuan authored and mhamilton723 committed Sep 13, 2018
1 parent 1996282 commit ef8cc20d1e056be030c2eba8518f545f1997504e
Showing with 10 additions and 2 deletions.
  1. +4 −1 README.md
  2. +6 −1 docs/lightgbm.md
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@@ -193,7 +193,7 @@ attached to all clusters you create.
Finally, ensure that your Spark cluster has at least Spark 2.1 and Scala 2.11.
You can use MMLSpark in both your Scala and PySpark notebooks. To get started with our example notebooks import the following databricks archive:
You can use MMLSpark in both your Scala and PySpark notebooks. To get started with our example notebooks import the following databricks archive:
```https://mmlspark.blob.core.windows.net/dbcs/MMLSpark%20Examples%20v0.13.dbc```
@@ -273,6 +273,9 @@ Issue](https://help.github.com/articles/creating-an-issue/).
* [Azure Machine Learning
preview features](https://docs.microsoft.com/en-us/azure/machine-learning/preview)
* [JPMML-SparkML plugin for converting MMLSpark LightGBM models to
PMML](https://github.com/alipay/jpmml-sparkml-lightgbm)
*Apache®, Apache Spark, and Spark® are either registered trademarks or
trademarks of the Apache Software Foundation in the United States and/or other
countries.*
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@@ -6,7 +6,7 @@
distributed, high-performance gradient boosting (GBDT, GBRT, GBM, or
MART) framework. This framework specializes in creating high-quality and
GPU enabled decision tree algorithms for ranking, classification, and
many other machine learning tasks. Light GBM is part of Microsoft's
many other machine learning tasks. LightGBM is part of Microsoft's
[DMTK](http://github.com/microsoft/dmtk) project.
### Advantages of LightGBM
@@ -69,3 +69,8 @@ The `LightGBMClassifier` and `LightGBMRegressor` use the SparkML API,
inherit from the same base classes, integrate with SparkML pipelines,
and can be tuned with [SparkML's cross
validators](https://spark.apache.org/docs/latest/ml-tuning.html).
Models built can be saved as SparkML pipeline with native LightGBM model
using `saveNativeModel()`. Additionally, they are fully compatible with [PMML](https://en.wikipedia.org/wiki/Predictive_Model_Markup_Language) and
can be converted to PMML format through the
[JPMML-SparkML-LightGBM](https://github.com/alipay/jpmml-sparkml-lightgbm) plugin.

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