docs: update LightGBM links (#2512)

Author: jameslamb

README.md

@@ -329,7 +329,7 @@ Issue](https://help.github.com/articles/creating-an-issue/).
 
 - [Vowpal Wabbit](https://github.com/VowpalWabbit/vowpal_wabbit)
 
-- [LightGBM](https://github.com/Microsoft/LightGBM)
+- [LightGBM](https://github.com/lightgbm-org/LightGBM)
 
 - [DMTK: Microsoft Distributed Machine Learning Toolkit](https://github.com/Microsoft/DMTK)
 

docs/Explore Algorithms/LightGBM/Overview.md

@@ -8,7 +8,7 @@ sidebar_label: Overview
 
 ### LightGBM
 
-[LightGBM](https://github.com/Microsoft/LightGBM) is an open-source,
+[LightGBM](https://github.com/lightgbm-org/LightGBM) is an open-source,
 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
@@ -22,11 +22,11 @@ many other machine learning tasks. LightGBM is part of Microsoft's
     workloads.
 -   **Performance**: LightGBM on Spark is 10-30% faster than SparkML on
     the Higgs dataset, and achieves a 15% increase in AUC.  [Parallel
-    experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment)
+    experiments](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment)
     have verified that LightGBM can achieve a linear speed-up by using
     multiple machines for training in specific settings.
 -   **Functionality**: LightGBM offers a wide array of [tunable
-    parameters](https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst),
+    parameters](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parameters.rst),
     that one can use to customize their decision tree system. LightGBM on
     Spark also supports new types of problems such as quantile regression.
 -   **Cross platform** LightGBM on Spark is available on Spark, PySpark, and SparklyR
@@ -107,10 +107,10 @@ both places, *passThroughArgs* takes precedence.
 LightGBM on Spark uses the Simple Wrapper and Interface Generator (SWIG)
 to add Java support for LightGBM. These Java Binding use the Java Native
 Interface call into the [distributed C++
-API](https://github.com/Microsoft/LightGBM/blob/master/include/LightGBM/c_api.h).
+API](https://github.com/lightgbm-org/LightGBM/blob/master/include/LightGBM/c_api.h).
 
 We initialize LightGBM by calling
-[`LGBM_NetworkInit`](https://github.com/Microsoft/LightGBM/blob/master/include/LightGBM/c_api.h)
+[`LGBM_NetworkInit`](https://github.com/lightgbm-org/LightGBM/blob/master/include/LightGBM/c_api.h)
 with the Spark executors within a MapPartitions call. We then pass each
 workers partitions into LightGBM to create the in-memory distributed
 dataset for LightGBM.  We can then train LightGBM to produce a model
@@ -201,7 +201,7 @@ For streaming mode, only one Dataset is created per partition, so *useSingleData
 ### Data Sampling
 
 In order for LightGBM algorithm to work, it must first create a set of bin boundaries for optimization. It does this calculation by
-first sampling the data before any training or inferencing starts. ([LightGBM docs](https://github.com/Microsoft/LightGBM)). The number of
+first sampling the data before any training or inferencing starts. ([LightGBM docs](https://github.com/lightgbm-org/LightGBM)). The number of
 samples to use is set using *binSampleCount*, which must be a minimal percent of the data or LightGBM rejects it.
 
 For *bulk* mode, this sampling is automatically done over the entire data, and each executor uses its own partitions to calculate samples for only

docs/Explore Algorithms/LightGBM/Quickstart - Classification, Ranking, and Regression.ipynb

@@ -14,7 +14,7 @@
    "source": [
     "## What is LightGBM\n",
     "\n",
-    "[LightGBM](https://github.com/Microsoft/LightGBM) is an open-source,\n",
+    "[LightGBM](https://github.com/lightgbm-org/LightGBM) is an open-source,\n",
     "distributed, high-performance gradient boosting (GBDT, GBRT, GBM, or\n",
     "MART) framework. This framework specializes in creating high-quality and\n",
     "GPU-enabled decision tree algorithms for ranking, classification, and\n",
@@ -28,11 +28,11 @@
     "    workloads.\n",
     "-   **Performance**: LightGBM on Spark is 10-30% faster than SparkML on\n",
     "    the [Higgs dataset](https://archive.ics.uci.edu/dataset/280/higgs) and achieves a 15% increase in AUC.  [Parallel\n",
-    "    experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment)\n",
+    "    experiments](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment)\n",
     "    have verified that LightGBM can achieve a linear speed-up by using\n",
     "    multiple machines for training in specific settings.\n",
     "-   **Functionality**: LightGBM offers a wide array of [tunable\n",
-    "    parameters](https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst),\n",
+    "    parameters](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parameters.rst),\n",
     "    that one can use to customize their decision tree system. LightGBM on\n",
     "    Spark also supports new types of problems such as quantile regression.\n",
     "-   **Cross platform**: LightGBM on Spark is available on Spark, PySpark, and SparklyR.\n",

docs/Explore Algorithms/Regression/Quickstart - Vowpal Wabbit and LightGBM.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "This notebook shows how to build simple regression models by using \n",
     "[Vowpal Wabbit (VW)](https://github.com/VowpalWabbit/vowpal_wabbit) and \n",
-    "[LightGBM](https://github.com/microsoft/LightGBM) with SynapseML.\n",
+    "[LightGBM](https://github.com/lightgbm-org/LightGBM) with SynapseML.\n",
     " We also compare the results with \n",
     " [Spark MLlib Linear Regression](https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression)."
    ]

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMClassifier.scala

@@ -20,8 +20,8 @@ object LightGBMClassifier extends DefaultParamsReadable[LightGBMClassifier]
 
 /** Trains a LightGBM Classification model, a fast, distributed, high performance gradient boosting
   * framework based on decision tree algorithms.
-  * For more information please see here: https://github.com/Microsoft/LightGBM.
-  * For parameter information see here: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst
+  * For more information please see here: https://github.com/lightgbm-org/LightGBM.
+  * For parameter information see here: https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parameters.rst
   * @param uid The unique ID.
   */
 class LightGBMClassifier(override val uid: String)

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMClassifier.txt

@@ -1,3 +1,3 @@
 Trains a LightGBM Classification model, a fast, distributed, high performance gradient boosting
 framework based on decision tree algorithms.
-For more information please see here: https://github.com/Microsoft/LightGBM.
+For more information please see here: https://github.com/lightgbm-org/LightGBM.

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRanker.scala

@@ -19,8 +19,8 @@ object LightGBMRanker extends DefaultParamsReadable[LightGBMRanker]
 
 /** Trains a LightGBMRanker model, a fast, distributed, high performance gradient boosting
   * framework based on decision tree algorithms.
-  * For more information please see here: https://github.com/Microsoft/LightGBM.
-  * For parameter information see here: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst
+  * For more information please see here: https://github.com/lightgbm-org/LightGBM.
+  * For parameter information see here: https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parameters.rst
   * @param uid The unique ID.
   */
 class LightGBMRanker(override val uid: String)

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRanker.txt

@@ -1,3 +1,3 @@
 Trains a LightGBM Ranker model, a fast, distributed, high performance gradient boosting
 framework based on decision tree algorithms.
-For more information please see here: https://github.com/Microsoft/LightGBM.
+For more information please see here: https://github.com/lightgbm-org/LightGBM.

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRegressor.scala

@@ -20,8 +20,8 @@ object LightGBMRegressor extends DefaultParamsReadable[LightGBMRegressor]
 
 /** Trains a LightGBM Regression model, a fast, distributed, high performance gradient boosting
   * framework based on decision tree algorithms.
-  * For more information please see here: https://github.com/Microsoft/LightGBM.
-  * For parameter information see here: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst
+  * For more information please see here: https://github.com/lightgbm-org/LightGBM.
+  * For parameter information see here: https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parameters.rst
   * Note: The application parameter supports the following values:
   *  - regression_l2, L2 loss, alias=regression, mean_squared_error, mse, l2_root, root_mean_squared_error, rmse
   *  - regression_l1, L1 loss, alias=mean_absolute_error, mae

lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRegressor.txt

@@ -1,3 +1,3 @@
 Trains a LightGBM Regression model, a fast, distributed, high performance gradient boosting
 framework based on decision tree algorithms.
-For more information please see here: https://github.com/Microsoft/LightGBM.
+For more information please see here: https://github.com/lightgbm-org/LightGBM.