@@ -24,7 +24,7 @@ For users who wants to install online with GPU or want to choose a specific comp
#### Mac OS Preparation
You can perform installation either with **Apple Clang** or **gcc**. In case you prefer **Apple Clang**, you should install **OpenMP** (details for installation can be found in [Installation Guide](https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#apple-clang)) first and **CMake** version 3.12 or higher is required. In case you prefer **gcc**, you need to install it (details for installation can be found in [Installation Guide](https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#gcc)) and set some environment variables to tell R to use `gcc` and `g++`. If you install these from Homebrew, your versions of `g++` and `gcc` are most likely in `/usr/local/bin`, as shown below.
You can perform installation either with **Apple Clang** or **gcc**. In case you prefer **Apple Clang**, you should install **OpenMP** (details for installation can be found in [Installation Guide](https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#apple-clang)) first and **CMake** version 3.12 or higher is required. In case you prefer **gcc**, you need to install it (details for installation can be found in [Installation Guide](https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#gcc)) and set some environment variables to tell R to use `gcc` and `g++`. If you install these from Homebrew, your versions of `g++` and `gcc` are most likely in `/usr/local/bin`, as shown below.
```
# replace 8 with version of gcc installed on your machine
@@ -47,7 +47,7 @@ Note: for the build with Visual Studio/VS Build Tools in Windows, you should use
Windows users may need to run with administrator rights (either R or the command prompt, depending on the way you are installing this package). Linux users might require the appropriate user write permissions for packages.
Set `use_gpu` to `TRUE` in `R-package/src/install.libs.R` to enable the build with GPU support. You will need to install Boost and OpenCL first: details for installation can be found in [Installation-Guide](https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#build-gpu-version).
Set `use_gpu` to `TRUE` in `R-package/src/install.libs.R` to enable the build with GPU support. You will need to install Boost and OpenCL first: details for installation can be found in [Installation-Guide](https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#build-gpu-version).
If you are using a precompiled dll/lib locally, you can move the dll/lib into LightGBM root folder, modify `LightGBM/R-package/src/install.libs.R`'s 2nd line (change `use_precompile <- FALSE` to `use_precompile <- TRUE`), and install R-package as usual. **NOTE: If your R version is not smaller than 3.5.0, you should set `DUSE_R35=ON` in cmake options when build precompiled dll/lib**.
...
...
@@ -89,7 +89,7 @@ For instance, you can install the R-package from LightGBM master commit of GitHu
```r
lgb.dl(commit="master",
compiler="vs",
repo="https://github.com/Microsoft/LightGBM")
repo="https://github.com/microsoft/LightGBM")
```
You may also install using a precompiled dll/lib using the following from R:
...
...
@@ -97,7 +97,7 @@ You may also install using a precompiled dll/lib using the following from R:
```r
lgb.dl(commit="master",
libdll="C:\\LightGBM\\windows\\x64\\DLL\\lib_lightgbm.dll",# YOUR PRECOMPILED DLL
repo="https://github.com/Microsoft/LightGBM")
repo="https://github.com/microsoft/LightGBM")
```
You may also install online using a LightGBM with proper GPU support using Visual Studio (as an example here) using the following from R:
...
...
@@ -105,13 +105,13 @@ You may also install online using a LightGBM with proper GPU support using Visua
```r
lgb.dl(commit="master",
compiler="vs",# Remove this for MinGW + GPU installation
repo="https://github.com/Microsoft/LightGBM",
repo="https://github.com/microsoft/LightGBM",
use_gpu=TRUE)
```
For more details about options, please check [Laurae2/lgbdl](https://github.com/Laurae2/lgbdl/) R-package.
You may also read [Microsoft/LightGBM#912](https://github.com/Microsoft/LightGBM/issues/912#issuecomment-329496254) for a visual example for LightGBM installation in Windows with Visual Studio.
You may also read [Microsoft/LightGBM#912](https://github.com/microsoft/LightGBM/issues/912#issuecomment-329496254) for a visual example for LightGBM installation in Windows with Visual Studio.
[](https://gitter.im/Microsoft/LightGBM?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
...
...
@@ -20,11 +20,11 @@ LightGBM is a gradient boosting framework that uses tree based learning algorith
- Support of parallel and GPU learning.
- Capable of handling large-scale data.
For further details, please refer to [Features](https://github.com/Microsoft/LightGBM/blob/master/docs/Features.rst).
For further details, please refer to [Features](https://github.com/microsoft/LightGBM/blob/master/docs/Features.rst).
Benefitting from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/Microsoft/LightGBM/blob/master/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions.
Benefitting from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/microsoft/LightGBM/blob/master/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions.
[Comparison experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, [parallel experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
[Comparison experiments](https://github.com/microsoft/LightGBM/blob/master/docs/Experiments.rst#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, [parallel experiments](https://github.com/microsoft/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
News
----
...
...
@@ -45,13 +45,13 @@ News
02/12/2017 : LightGBM v1 stable release.
01/08/2017 : Release [**R-package**](https://github.com/Microsoft/LightGBM/tree/master/R-package) beta version, welcome to have a try and provide feedback.
01/08/2017 : Release [**R-package**](https://github.com/microsoft/LightGBM/tree/master/R-package) beta version, welcome to have a try and provide feedback.
12/05/2016 : **Categorical Features as input directly** (without one-hot coding).
12/02/2016 : Release [**Python-package**](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide feedback.
12/02/2016 : Release [**Python-package**](https://github.com/microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide feedback.
More detailed update logs : [Key Events](https://github.com/Microsoft/LightGBM/blob/master/docs/Key-Events.md).
More detailed update logs : [Key Events](https://github.com/microsoft/LightGBM/blob/master/docs/Key-Events.md).
External (Unofficial) Repositories
----------------------------------
...
...
@@ -77,22 +77,22 @@ Dask-LightGBM (distributed and parallel Python-package): https://github.com/dask
Get Started and Documentation
-----------------------------
Install by following [guide](https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst) for the command line program, [Python-package](https://github.com/Microsoft/LightGBM/tree/master/python-package) or [R-package](https://github.com/Microsoft/LightGBM/tree/master/R-package). Then please see the [Quick Start](https://github.com/Microsoft/LightGBM/blob/master/docs/Quick-Start.rst) guide.
Install by following [guide](https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst) for the command line program, [Python-package](https://github.com/microsoft/LightGBM/tree/master/python-package) or [R-package](https://github.com/microsoft/LightGBM/tree/master/R-package). Then please see the [Quick Start](https://github.com/microsoft/LightGBM/blob/master/docs/Quick-Start.rst) guide.
Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository.
Next you may want to read:
*[**Examples**](https://github.com/Microsoft/LightGBM/tree/master/examples) showing command line usage of common tasks.
*[**Features**](https://github.com/Microsoft/LightGBM/blob/master/docs/Features.rst) and algorithms supported by LightGBM.
*[**Parameters**](https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst) is an exhaustive list of customization you can make.
*[**Parallel Learning**](https://github.com/Microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/Microsoft/LightGBM/blob/master/docs/GPU-Tutorial.rst) can speed up computation.
*[**Examples**](https://github.com/microsoft/LightGBM/tree/master/examples) showing command line usage of common tasks.
*[**Features**](https://github.com/microsoft/LightGBM/blob/master/docs/Features.rst) and algorithms supported by LightGBM.
*[**Parameters**](https://github.com/microsoft/LightGBM/blob/master/docs/Parameters.rst) is an exhaustive list of customization you can make.
*[**Parallel Learning**](https://github.com/microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/microsoft/LightGBM/blob/master/docs/GPU-Tutorial.rst) can speed up computation.
*[**Laurae++ interactive documentation**](https://sites.google.com/view/lauraepp/parameters) is a detailed guide for hyperparameters.
Documentation for contributors:
*[**How we update readthedocs.io**](https://github.com/Microsoft/LightGBM/blob/master/docs/README.rst).
* Check out the [**Development Guide**](https://github.com/Microsoft/LightGBM/blob/master/docs/Development-Guide.rst).
*[**How we update readthedocs.io**](https://github.com/microsoft/LightGBM/blob/master/docs/README.rst).
* Check out the [**Development Guide**](https://github.com/microsoft/LightGBM/blob/master/docs/Development-Guide.rst).
Support
-------
...
...
@@ -101,18 +101,18 @@ Support
* Discuss on the [LightGBM Gitter](https://gitter.im/Microsoft/LightGBM).
* Discuss on the [LightGBM Slack team](https://lightgbm.slack.com).
* Use [this invite link](https://lightgbm-slack-autojoin.herokuapp.com/) to join the team.
* Open **bug reports** and **feature requests** (not questions) on [GitHub issues](https://github.com/Microsoft/LightGBM/issues).
* Open **bug reports** and **feature requests** (not questions) on [GitHub issues](https://github.com/microsoft/LightGBM/issues).
How to Contribute
-----------------
LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
- Check out [call for contributions](https://github.com/Microsoft/LightGBM/issues?q=is%3Aissue+is%3Aopen+label%3Acall-for-contribution) to see what can be improved, or open an issue if you want something.
- Contribute to the [tests](https://github.com/Microsoft/LightGBM/tree/master/tests) to make it more reliable.
- Contribute to the [documents](https://github.com/Microsoft/LightGBM/tree/master/docs) to make it clearer for everyone.
- Contribute to the [examples](https://github.com/Microsoft/LightGBM/tree/master/examples) to share your experience with other users.
- Add your stories and experience to [Awesome LightGBM](https://github.com/Microsoft/LightGBM/blob/master/examples/README.md).
- Check out [call for contributions](https://github.com/microsoft/LightGBM/issues?q=is%3Aissue+is%3Aopen+label%3Acall-for-contribution) to see what can be improved, or open an issue if you want something.
- Contribute to the [tests](https://github.com/microsoft/LightGBM/tree/master/tests) to make it more reliable.
- Contribute to the [documents](https://github.com/microsoft/LightGBM/tree/master/docs) to make it clearer for everyone.
- Contribute to the [examples](https://github.com/microsoft/LightGBM/tree/master/examples) to share your experience with other users.
- Add your stories and experience to [Awesome LightGBM](https://github.com/microsoft/LightGBM/blob/master/examples/README.md).
- Open issue if you met problems during development.
Microsoft Open Source Code of Conduct
...
...
@@ -132,4 +132,4 @@ Huan Zhang, Si Si and Cho-Jui Hsieh. "[GPU Acceleration for Large-scale Tree Boo
License
-------
This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/Microsoft/LightGBM/blob/master/LICENSE) for additional details.
This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/microsoft/LightGBM/blob/master/LICENSE) for additional details.
@@ -73,16 +73,16 @@ Refer to `docs README <./README.rst>`__.
C API
-----
Refer to `C API <./C-API.rst>`__ or the comments in `c\_api.h <https://github.com/Microsoft/LightGBM/blob/master/include/LightGBM/c_api.h>`__ file, from which the documentation is generated.
Refer to `C API <./C-API.rst>`__ or the comments in `c\_api.h <https://github.com/microsoft/LightGBM/blob/master/include/LightGBM/c_api.h>`__ file, from which the documentation is generated.
High Level Language Package
---------------------------
See the implementations at `Python-package <https://github.com/Microsoft/LightGBM/tree/master/python-package>`__ and `R-package <https://github.com/Microsoft/LightGBM/tree/master/R-package>`__.
See the implementations at `Python-package <https://github.com/microsoft/LightGBM/tree/master/python-package>`__ and `R-package <https://github.com/microsoft/LightGBM/tree/master/R-package>`__.
Questions
---------
Refer to `FAQ <./FAQ.rst>`__.
Also feel free to open `issues <https://github.com/Microsoft/LightGBM/issues>`__ if you met problems.
Also feel free to open `issues <https://github.com/microsoft/LightGBM/issues>`__ if you met problems.
Please post an issue in `Microsoft/LightGBM repository <https://github.com/Microsoft/LightGBM/issues>`__ for any
Please post an issue in `Microsoft/LightGBM repository <https://github.com/microsoft/LightGBM/issues>`__ for any
LightGBM issues you encounter. For critical issues (crash, prediction error, nonsense outputs...), you may also ping a
member of the core team according to the relevant area of expertise by mentioning them with the arobase (@) symbol:
...
...
@@ -65,20 +65,20 @@ LightGBM
- **Question 3**: When running LightGBM on a large dataset, my computer runs out of RAM.
- **Solution 3**: Multiple solutions: set the ``histogram_pool_size`` parameter to the MB you want to use for LightGBM (histogram\_pool\_size + dataset size = approximately RAM used),
lower ``num_leaves`` or lower ``max_bin`` (see `Microsoft/LightGBM#562 <https://github.com/Microsoft/LightGBM/issues/562>`__).
lower ``num_leaves`` or lower ``max_bin`` (see `Microsoft/LightGBM#562 <https://github.com/microsoft/LightGBM/issues/562>`__).
--------------
- **Question 4**: I am using Windows. Should I use Visual Studio or MinGW for compiling LightGBM?
- **Solution 4**: Visual Studio `performs best for LightGBM <https://github.com/Microsoft/LightGBM/issues/542>`__.
- **Solution 4**: Visual Studio `performs best for LightGBM <https://github.com/microsoft/LightGBM/issues/542>`__.
--------------
- **Question 5**: When using LightGBM GPU, I cannot reproduce results over several runs.
- **Solution 5**: This is normal and expected behaviour, but you may try to use ``gpu_use_dp = true`` for reproducibility
(see `Microsoft/LightGBM#560 <https://github.com/Microsoft/LightGBM/pull/560#issuecomment-304561654>`__).
(see `Microsoft/LightGBM#560 <https://github.com/microsoft/LightGBM/pull/560#issuecomment-304561654>`__).
You may also use the CPU version.
--------------
...
...
@@ -86,7 +86,7 @@ LightGBM
- **Question 6**: Bagging is not reproducible when changing the number of threads.
- **Solution 6**: LightGBM bagging is multithreaded, so its output depends on the number of threads used.
There is `no workaround currently <https://github.com/Microsoft/LightGBM/issues/632>`__.
There is `no workaround currently <https://github.com/microsoft/LightGBM/issues/632>`__.
--------------
...
...
@@ -94,14 +94,14 @@ LightGBM
- **Solution 7**: This is expected behaviour for arbitrary parameters. To enable Random Forest,
you must use ``bagging_fraction`` and ``feature_fraction`` different from 1, along with a ``bagging_freq``.
`This thread <https://github.com/Microsoft/LightGBM/issues/691>`__ includes an example.
`This thread <https://github.com/microsoft/LightGBM/issues/691>`__ includes an example.
--------------
- **Question 8**: CPU usage is low (like 10%) in Windows when using LightGBM on very large datasets with many-core systems.
- **Solution 8**: Please use `Visual Studio <https://visualstudio.microsoft.com/downloads/>`__
as it may be `10x faster than MinGW <https://github.com/Microsoft/LightGBM/issues/749>`__ especially for very large trees.
as it may be `10x faster than MinGW <https://github.com/microsoft/LightGBM/issues/749>`__ especially for very large trees.
--------------
...
...
@@ -115,7 +115,7 @@ LightGBM
- **Solution 9**: The column you're trying to pass via ``categorical_feature`` likely contains very large values.
Categorical features in LightGBM are limited by int32 range,
so you cannot pass values that are greater than ``Int32.MaxValue`` (2147483647) as categorical features (see `Microsoft/LightGBM#1359 <https://github.com/Microsoft/LightGBM/issues/1359>`__).
so you cannot pass values that are greater than ``Int32.MaxValue`` (2147483647) as categorical features (see `Microsoft/LightGBM#1359 <https://github.com/microsoft/LightGBM/issues/1359>`__).
You should convert them to integers ranging from zero to the number of categories first.
--------------
...
...
@@ -145,7 +145,7 @@ LightGBM
- **Question 11**: LightGBM hangs when multithreading (OpenMP) and using forking in Linux at the same time.
- **Solution 11**: Use ``nthreads=1`` to disable multithreading of LightGBM. There is a bug with OpenMP which hangs forked sessions with multithreading activated. A more expensive solution is to use new processes instead of using fork, however, keep in mind it is creating new processes where you have to copy memory and load libraries (example: if you want to fork 16 times your current process, then you will require to make 16 copies of your dataset in memory) (see `Microsoft/LightGBM#1789 <https://github.com/Microsoft/LightGBM/issues/1789#issuecomment-433713383>`__).
- **Solution 11**: Use ``nthreads=1`` to disable multithreading of LightGBM. There is a bug with OpenMP which hangs forked sessions with multithreading activated. A more expensive solution is to use new processes instead of using fork, however, keep in mind it is creating new processes where you have to copy memory and load libraries (example: if you want to fork 16 times your current process, then you will require to make 16 copies of your dataset in memory) (see `Microsoft/LightGBM#1789 <https://github.com/microsoft/LightGBM/issues/1789#issuecomment-433713383>`__).
An alternative, if multithreading is really necessary inside the forked sessions, would be to compile LightGBM with Intel toolchain. Intel compilers are unaffected by this bug.
...
...
@@ -160,14 +160,14 @@ R-package
- **Solution 1**: Run ``lgb.unloader(wipe = TRUE)`` in the R console, and recreate the LightGBM datasets (this will wipe all LightGBM-related variables).
Due to the pointers, choosing to not wipe variables will not fix the error.
This is a known issue: `Microsoft/LightGBM#698 <https://github.com/Microsoft/LightGBM/issues/698>`__.
This is a known issue: `Microsoft/LightGBM#698 <https://github.com/microsoft/LightGBM/issues/698>`__.
--------------
- **Question 2**: I used ``setinfo``, tried to print my ``lgb.Dataset``, and now the R console froze!
- **Solution 2**: Avoid printing the ``lgb.Dataset`` after using ``setinfo``.
This is a known bug: `Microsoft/LightGBM#539 <https://github.com/Microsoft/LightGBM/issues/539>`__.
This is a known bug: `Microsoft/LightGBM#539 <https://github.com/microsoft/LightGBM/issues/539>`__.
--------------
...
...
@@ -226,9 +226,9 @@ Python-package
- **Question 3**: I encounter segmentation faults (segfaults) randomly after installing LightGBM from PyPI using ``pip install lightgbm``.
- **Solution 3**: We are doing our best to provide universal wheels which have high running speed and are compatible with any hardware, OS, compiler, etc. at the same time.
However, sometimes it's just impossible to guarantee the possibility of usage of LightGBM in any specific environment (see `Microsoft/LightGBM#1743 <https://github.com/Microsoft/LightGBM/issues/1743>`__).
However, sometimes it's just impossible to guarantee the possibility of usage of LightGBM in any specific environment (see `Microsoft/LightGBM#1743 <https://github.com/microsoft/LightGBM/issues/1743>`__).
Therefore, the first thing you should try in case of segfaults is **compiling from the source** using ``pip install --no-binary :all: lightgbm``.
For the OS-specific prerequisites see `this guide <https://github.com/Microsoft/LightGBM/blob/master/python-package/README.rst#build-from-sources>`__.
For the OS-specific prerequisites see `this guide <https://github.com/microsoft/LightGBM/blob/master/python-package/README.rst#build-from-sources>`__.
Also, feel free to post a new issue in our GitHub repository. We always look at each case individually and try to find a root cause.
| 21/05/2017 | [4cf9376](https://github.com/Microsoft/LightGBM/commit/4cf9376d6652d3d7afa82e98dfb363af9275969d) | Feature | Limitation of threads for dataset construction (Commit) |
| 15/05/2017 | [e984b0d](https://github.com/Microsoft/LightGBM/pull/516) | Feature | Support for missing values (Pull Request 516) |
| 28/01/2017 | [5856554](https://github.com/Microsoft/LightGBM/pull/266) | Feature | Plot Metric support for Python-package (Pull Request 266) |
| 25/01/2017 | [8980fc7](https://github.com/Microsoft/LightGBM/pull/262) | Feature | Plot Tree support for Python-package (Pull Request 262)<br>With original ([fafbcb3](https://github.com/Microsoft/LightGBM/pull/258) on 25/01/2017, Pull Request 258) |
| 21/05/2017 | [4cf9376](https://github.com/microsoft/LightGBM/commit/4cf9376d6652d3d7afa82e98dfb363af9275969d) | Feature | Limitation of threads for dataset construction (Commit) |
| 15/05/2017 | [e984b0d](https://github.com/microsoft/LightGBM/pull/516) | Feature | Support for missing values (Pull Request 516) |
| 28/01/2017 | [5856554](https://github.com/microsoft/LightGBM/pull/266) | Feature | Plot Metric support for Python-package (Pull Request 266) |
| 25/01/2017 | [8980fc7](https://github.com/microsoft/LightGBM/pull/262) | Feature | Plot Tree support for Python-package (Pull Request 262)<br>With original ([fafbcb3](https://github.com/microsoft/LightGBM/pull/258) on 25/01/2017, Pull Request 258) |
* 18/06/2017 [Microsoft/LightGBM@4d2aa84](https://github.com/Microsoft/LightGBM/pull/634): CIntegration: AppVeyor Support (Pull Request 634)
* 06/06/2017 [Microsoft/LightGBM@2c9ce59](https://github.com/Microsoft/LightGBM/pull/592): Release: R-package version 0.2 (Pull Request 592)
* 05/06/2017 [Microsoft/LightGBM@f98d75f](https://github.com/Microsoft/LightGBM/pull/584): Feature: Use custom compiler for R-package (Pull Request 584)
* 29/05/2017 [Microsoft/LightGBM@993bbd5](https://github.com/Microsoft/LightGBM/pull/559): Parameter: Early Stopping for predictions (Pull Request 559)
* 26/05/2017 [Microsoft/LightGBM@3abff37](https://github.com/Microsoft/LightGBM/commit/3abff370bb353293e4a03e516111dd02785fbd97): Feature: Parameter to disable missing values (Commit)
* 21/05/2017 [Microsoft/LightGBM@4cf9376](https://github.com/Microsoft/LightGBM/commit/4cf9376d6652d3d7afa82e98dfb363af9275969d): Feature: Limitation of threads for dataset construction (Commit)
* 15/05/2017 [Microsoft/LightGBM@e984b0d](https://github.com/Microsoft/LightGBM/pull/516): Feature: Support for missing values (Pull Request 516)
* 04/05/2017 [Microsoft/LightGBM@ca30b8c](https://github.com/Microsoft/LightGBM/pull/489): Docs: Read The Docs (Pull Request 489)
* 28/04/2017 [Microsoft/LightGBM@8a19834](https://github.com/Microsoft/LightGBM/pull/469): Feature: If-Then-Else C++ Trees Generation (Pull Request 469)
* 18/04/2017 ([Microsoft/LightGBM@7339ed6](https://github.com/Microsoft/LightGBM/pull/426)): Feature: Whitespaces Removed from Features support (Pull Request 426)
* 14/04/2017 ([Microsoft/LightGBM@9224a9d](https://github.com/Microsoft/LightGBM/pull/415)): Release: GPU support (Pull Request 415) with original ([Microsoft/LightGBM@0bb4a82](https://github.com/Microsoft/LightGBM/pull/368) on 09/04/2017, Pull Request 368)
* 02/02/2017 ([Microsoft/LightGBM@8c8ed6c](https://github.com/Microsoft/LightGBM/pull/275)): Feature: Docker support (Pull Request 275)
* 30/01/2017 ([Microsoft/LightGBM@4f3e9d8](https://github.com/Microsoft/LightGBM/pull/270)): Feature: Poisson Objective support (Pull Request 270)
* 28/01/2017 ([Microsoft/LightGBM@5856554](https://github.com/Microsoft/LightGBM/pull/266)): Feature: Plot Metric support for Python-package (Pull Request 266)
* 25/01/2017 ([Microsoft/LightGBM@8980fc7](https://github.com/Microsoft/LightGBM/pull/262)): Feature: Plot Tree support for Python-package (Pull Request 262) with original ([Microsoft/LightGBM@fafbcb3](https://github.com/Microsoft/LightGBM/pull/258) on 25/01/2017, Pull Request 258)
* 16/01/2017 ([Microsoft/LightGBM@a2ae838](https://github.com/Microsoft/LightGBM/pull/229)): Feature: Categorical Feature support for R-package (Pull Request 229)
* 16/01/2017 ([Microsoft/LightGBM@57d5527](https://github.com/Microsoft/LightGBM/pull/218)): Feature: Pandas Categorical support for Python-package (Pull Request 193) with original ([Microsoft/LightGBM@a2ae838](https://github.com/Microsoft/LightGBM/pull/193) on 12/01/2017, Pull Request 193)
* 10/01/2017 ([Microsoft/LightGBM@fb732c3](https://github.com/Microsoft/LightGBM/pull/180)): Feature: Fair Loss Objective support (Pull Request 180)
* 09/01/2017 ([Microsoft/LightGBM@27d3eb3](https://github.com/Microsoft/LightGBM/pull/178)): Feature: Huber Loss Objective support (Pull Request 178) with original ([Microsoft/LightGBM@a87af87](https://github.com/Microsoft/LightGBM/pull/174) on 09/01/2017, Pull Request 174)
* 09/01/2017 ([Microsoft/LightGBM@9b2558d](https://github.com/Microsoft/LightGBM/pull/177)): Feature: devtools R Installation support (Pull Request 177)
* 09/01/2017 ([Microsoft/LightGBM@6219df7](https://github.com/Microsoft/LightGBM/pull/175)): Feature: L1 Loss Objective support (Pull Request 175)
* 08/01/2017 ([Microsoft/LightGBM@551d59c](https://github.com/Microsoft/LightGBM/pull/168)): Release: R support (Pull Request 168)
* 05/01/2017 ([Microsoft/LightGBM@96d08f4](https://github.com/Microsoft/LightGBM/pull/153)): Feature: PMML support (Pull Request 153)
* 01/01/2017 ([Microsoft/LightGBM@a034cee](https://github.com/Microsoft/LightGBM/pull/151)): Feature: Pickle support for Python-package (Pull Request 151)
* 26/12/2016 ([Microsoft/LightGBM@96cba41](https://github.com/Microsoft/LightGBM/pull/139)): Parameter: DART xgboost support (Pull Request 139)
* 19/12/2016 ([Microsoft/LightGBM@99b483d](https://github.com/Microsoft/LightGBM/pull/133)): Parameter: Learning Rate in DART support (Pull Request 133)
* 01/12/2016 ([Microsoft/LightGBM@16d1853](https://github.com/Microsoft/LightGBM/pull/94)): Release: Python support (Pull Request 94)
* 11/11/2016 ([Microsoft/LightGBM@98be7e3](https://github.com/Microsoft/LightGBM/pull/67)): Feature: DART booster support (Pull Request 67)
* 08/11/2016 ([Microsoft/LightGBM@785398a](https://github.com/Microsoft/LightGBM/pull/69)): Parameter: L1 Regularization, L2 Regularization, Minimum Gain to Split support (Pull Request 69)
* 05/11/2016 ([Microsoft/LightGBM@1466f90](https://github.com/Microsoft/LightGBM/pull/108)): Release: Categorical Feature support (Pull Request 108)
* 01/11/2016 ([Microsoft/LightGBM@aa796a8](https://github.com/Microsoft/LightGBM/pull/53)): Feature: Multiclass classification support (Pull Request 53)
* 28/10/2016 ([Microsoft/LightGBM@c45d1d9](https://github.com/Microsoft/LightGBM/pull/42)): Feature: Feature Importance support (Pull Request 42)
* 25/10/2016 ([Microsoft/LightGBM@a6a75fe](https://github.com/Microsoft/LightGBM/pull/35)): Parameter: Maximum Depth support (Pull Request 35)
* 24/10/2016 ([Microsoft/LightGBM@9fe0dea](https://github.com/Microsoft/LightGBM/pull/30)): Parameter: Leaf Index Prediction support (Pull Request 30)
* 21/10/2016 ([Microsoft/LightGBM@7aaba32](https://github.com/Microsoft/LightGBM/pull/27)): Parameter: Early Stopping support (Pull Request 27) with original ([Microsoft/LightGBM@7d4b6d4](https://github.com/Microsoft/LightGBM/pull/21) on 20/10/2017, Pull Request 21)
* 18/06/2017 [Microsoft/LightGBM@4d2aa84](https://github.com/microsoft/LightGBM/pull/634): CIntegration: AppVeyor Support (Pull Request 634)
* 06/06/2017 [Microsoft/LightGBM@2c9ce59](https://github.com/microsoft/LightGBM/pull/592): Release: R-package version 0.2 (Pull Request 592)
* 05/06/2017 [Microsoft/LightGBM@f98d75f](https://github.com/microsoft/LightGBM/pull/584): Feature: Use custom compiler for R-package (Pull Request 584)
* 29/05/2017 [Microsoft/LightGBM@993bbd5](https://github.com/microsoft/LightGBM/pull/559): Parameter: Early Stopping for predictions (Pull Request 559)
* 26/05/2017 [Microsoft/LightGBM@3abff37](https://github.com/microsoft/LightGBM/commit/3abff370bb353293e4a03e516111dd02785fbd97): Feature: Parameter to disable missing values (Commit)
* 21/05/2017 [Microsoft/LightGBM@4cf9376](https://github.com/microsoft/LightGBM/commit/4cf9376d6652d3d7afa82e98dfb363af9275969d): Feature: Limitation of threads for dataset construction (Commit)
* 15/05/2017 [Microsoft/LightGBM@e984b0d](https://github.com/microsoft/LightGBM/pull/516): Feature: Support for missing values (Pull Request 516)
* 04/05/2017 [Microsoft/LightGBM@ca30b8c](https://github.com/microsoft/LightGBM/pull/489): Docs: Read The Docs (Pull Request 489)
* 28/04/2017 [Microsoft/LightGBM@8a19834](https://github.com/microsoft/LightGBM/pull/469): Feature: If-Then-Else C++ Trees Generation (Pull Request 469)
* 18/04/2017 ([Microsoft/LightGBM@7339ed6](https://github.com/microsoft/LightGBM/pull/426)): Feature: Whitespaces Removed from Features support (Pull Request 426)
* 14/04/2017 ([Microsoft/LightGBM@9224a9d](https://github.com/microsoft/LightGBM/pull/415)): Release: GPU support (Pull Request 415) with original ([Microsoft/LightGBM@0bb4a82](https://github.com/microsoft/LightGBM/pull/368) on 09/04/2017, Pull Request 368)
* 02/02/2017 ([Microsoft/LightGBM@8c8ed6c](https://github.com/microsoft/LightGBM/pull/275)): Feature: Docker support (Pull Request 275)
* 30/01/2017 ([Microsoft/LightGBM@4f3e9d8](https://github.com/microsoft/LightGBM/pull/270)): Feature: Poisson Objective support (Pull Request 270)
* 28/01/2017 ([Microsoft/LightGBM@5856554](https://github.com/microsoft/LightGBM/pull/266)): Feature: Plot Metric support for Python-package (Pull Request 266)
* 25/01/2017 ([Microsoft/LightGBM@8980fc7](https://github.com/microsoft/LightGBM/pull/262)): Feature: Plot Tree support for Python-package (Pull Request 262) with original ([Microsoft/LightGBM@fafbcb3](https://github.com/microsoft/LightGBM/pull/258) on 25/01/2017, Pull Request 258)
* 16/01/2017 ([Microsoft/LightGBM@a2ae838](https://github.com/microsoft/LightGBM/pull/229)): Feature: Categorical Feature support for R-package (Pull Request 229)
* 16/01/2017 ([Microsoft/LightGBM@57d5527](https://github.com/microsoft/LightGBM/pull/218)): Feature: Pandas Categorical support for Python-package (Pull Request 193) with original ([Microsoft/LightGBM@a2ae838](https://github.com/microsoft/LightGBM/pull/193) on 12/01/2017, Pull Request 193)
* 10/01/2017 ([Microsoft/LightGBM@fb732c3](https://github.com/microsoft/LightGBM/pull/180)): Feature: Fair Loss Objective support (Pull Request 180)
* 09/01/2017 ([Microsoft/LightGBM@27d3eb3](https://github.com/microsoft/LightGBM/pull/178)): Feature: Huber Loss Objective support (Pull Request 178) with original ([Microsoft/LightGBM@a87af87](https://github.com/microsoft/LightGBM/pull/174) on 09/01/2017, Pull Request 174)
* 09/01/2017 ([Microsoft/LightGBM@9b2558d](https://github.com/microsoft/LightGBM/pull/177)): Feature: devtools R Installation support (Pull Request 177)
* 09/01/2017 ([Microsoft/LightGBM@6219df7](https://github.com/microsoft/LightGBM/pull/175)): Feature: L1 Loss Objective support (Pull Request 175)
* 08/01/2017 ([Microsoft/LightGBM@551d59c](https://github.com/microsoft/LightGBM/pull/168)): Release: R support (Pull Request 168)
* 05/01/2017 ([Microsoft/LightGBM@96d08f4](https://github.com/microsoft/LightGBM/pull/153)): Feature: PMML support (Pull Request 153)
* 01/01/2017 ([Microsoft/LightGBM@a034cee](https://github.com/microsoft/LightGBM/pull/151)): Feature: Pickle support for Python-package (Pull Request 151)
* 26/12/2016 ([Microsoft/LightGBM@96cba41](https://github.com/microsoft/LightGBM/pull/139)): Parameter: DART xgboost support (Pull Request 139)
* 19/12/2016 ([Microsoft/LightGBM@99b483d](https://github.com/microsoft/LightGBM/pull/133)): Parameter: Learning Rate in DART support (Pull Request 133)
* 01/12/2016 ([Microsoft/LightGBM@16d1853](https://github.com/microsoft/LightGBM/pull/94)): Release: Python support (Pull Request 94)
* 11/11/2016 ([Microsoft/LightGBM@98be7e3](https://github.com/microsoft/LightGBM/pull/67)): Feature: DART booster support (Pull Request 67)
* 08/11/2016 ([Microsoft/LightGBM@785398a](https://github.com/microsoft/LightGBM/pull/69)): Parameter: L1 Regularization, L2 Regularization, Minimum Gain to Split support (Pull Request 69)
* 05/11/2016 ([Microsoft/LightGBM@1466f90](https://github.com/microsoft/LightGBM/pull/108)): Release: Categorical Feature support (Pull Request 108)
* 01/11/2016 ([Microsoft/LightGBM@aa796a8](https://github.com/microsoft/LightGBM/pull/53)): Feature: Multiclass classification support (Pull Request 53)
* 28/10/2016 ([Microsoft/LightGBM@c45d1d9](https://github.com/microsoft/LightGBM/pull/42)): Feature: Feature Importance support (Pull Request 42)
* 25/10/2016 ([Microsoft/LightGBM@a6a75fe](https://github.com/microsoft/LightGBM/pull/35)): Parameter: Maximum Depth support (Pull Request 35)
* 24/10/2016 ([Microsoft/LightGBM@9fe0dea](https://github.com/microsoft/LightGBM/pull/30)): Parameter: Leaf Index Prediction support (Pull Request 30)
* 21/10/2016 ([Microsoft/LightGBM@7aaba32](https://github.com/microsoft/LightGBM/pull/27)): Parameter: Early Stopping support (Pull Request 27) with original ([Microsoft/LightGBM@7d4b6d4](https://github.com/microsoft/LightGBM/pull/21) on 20/10/2017, Pull Request 21)
