官术网_书友最值得收藏!

LightGBM

LightGBM is a gradient boosting framework that was developed by Microsoft that uses the tree-based learning algorithm in a different fashion than other GBMs, favoring exploration of more promising leaves (leaf-wise) instead of developing level-wise.


In graph terminology, LightGBM is pursuing a depth-first search strategy than a breadth-first search one.

It has been designed to be distributed (Parallel and GPU learning supported), and its unique approach really achieves faster training speed with lower memory usage (thus allowing for the handling of the larger scale of data):

The installation of XGBoost requires some more actions on your side than usual Python packages. If you are operating on a Windows system, open a shell and issue the following commands:

$> git clone --recursive https://github.com/Microsoft/LightGBM
$> cd LightGBM
$> mkdir build
$> cd build
$> cmake -G "MinGW Makefiles" ..
$> mingw32-make.exe -j4
You may need to install CMake on your system first ( https://cmake.org), and you also may need to run  cmake -G "MinGW Makefiles" .. if a sh.exe was found in your PATH error is reported.

If you are instead operating on a Linux system, you just need to digit on a shell:

$> git clone --recursive https://github.com/Microsoft/LightGBM
$> cd LightGBM
$> mkdir build
$> cd build
$> cmake ..
$> make -j4

After you have completed compiling the package, no matter whether you are on Windows or Linux, you just import it on your Python command line:

import lightgbm as lgbm
You can also build the package using MPI for parallel computing architectures, HDFS, or GPU versions. You can find all the detailed instructions at https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst.
主站蜘蛛池模板: 北碚区| 孙吴县| 克拉玛依市| 眉山市| 日喀则市| 博客| 历史| 南郑县| 紫云| 栾城县| 罗甸县| 富顺县| 平和县| 克什克腾旗| 女性| 沈丘县| 长子县| 疏附县| 正阳县| 玉田县| 开江县| 壶关县| 江西省| 沈阳市| 博兴县| 海阳市| 秦皇岛市| 祁东县| 达尔| 靖江市| 深圳市| 略阳县| 故城县| 克什克腾旗| 新蔡县| 南康市| 蓝田县| 永宁县| 呼和浩特市| 尼木县| 兴国县|