lightgbm

在之前的 XGBoost原理理解 中已经推导过XGBoost的决策树的分裂增益为 L s p l i t = 1 2 [ ( ∑ i ∈ I L g i ) 2 ∑ i ∈ I L h i + λ + ( ∑ i ∈ I R g i ) 2 ∑ i ∈ I R h i + λ − ( ∑ i ∈ I g i ) 2 ∑ i ∈ I h i + λ ] − γ \mathcal{L}_{split}={1\over2}\bigg[{(\sum_{i\in I_L}g_i)^2\over \sum_{i\in I_L}h_i+\lambda}+{(\sum_{i\in I_R}g_i)^2\over \sum_{i\in I_R}h_i+\lambda}-{(\sum_{i\in I}g_i)^2\over \sum_{i\in I}h_i+\lambda}\bigg]-\gamma L s p l i t ​ = 2 1 ​ [ ∑ i ∈ I L ​ ​ h i ​ + λ ( ∑ i ∈ I L ​ ​ g i ​ ) 2 ​ + ∑ i ∈ I R ​ ​ h i ​ + λ ( ∑ i ∈ I R ​ ​ g i ​ ) 2 ​ − ∑ i ∈ I ​ h i ​ + λ ( ∑ i ∈ I ​ g i ​ ) 2 ​ ] − γ 其中 λ \lambda λ

【推荐】2019 Java 开发者跳槽指南.pdf(吐血整理) >>> The gradient boosting decision tree (GBDT) is one of the best performing classes of algorithms in machine learning competitions. One implementation of the gradient boosting decision tree – xgboost – is one of the most popular algorithms on Kaggle. Among the 29 challenge winning solutions published at Kaggle’s blog during 2015, 17 used xgboost. If you take a look at the kernels in a Kaggle competition, you can clearly see how popular xgboost is. The search results for all kernels that had xgboost in their titles for the Kaggle Quora Duplicate Question

【推荐】2019 Java 开发者跳槽指南.pdf(吐血整理) >>> ## mmlspark https://mvnrepository.com/artifact/Azure/mmlspark/0.15 ## lightgbmlib https://mvnrepository.com/artifact/com.microsoft.ml.lightgbm/lightgbmlib/2.2.200 [root@hadoop-1-1 ~]# more lgbm.sh /app/spark2.3/bin/spark-submit \ --master yarn \ --jars /root/external_pkgs/mmlspark-0.15.jar,/root/external_pkgs/lightgbmlib-2.2.200.jar \ --class com.sf.demo.lgmClassifier /root/lgbm_demo.jar nohup sh lgbm.sh > lgbm_20191226_001.log 2>&1 & package com.xx.demo import org.apache.spark.SparkConf import org.apache.spark.sql.SparkSession import org.apache.spark.ml

GPU support for XGBoost and LightGBM GBDT 是表格型数据挖掘比赛的大杀器，其主要思想是利用弱分类器（决策树）迭代训练以得到最优模型，该模型具有训练效果好、不易过拟合等优点。XGBoost 和 LightGBM 是两个实现 GBDT 算法的框架，为了加快模型的训练效率，本文记录了 GPU Support 的 XGBoost and LightGBM 的构建过程。 本次构建的系统环境为 CentOS 7.2。 Installation Guide for XGBoost GPU support Building XGBoost from source，构建和安装 XGBoost 包括如下两个步骤， 从 C++ 代码构建共享库（libxgboost.so for Linux/OSX and xgboost.dll for Windows） 安装语言包（如Python） Building the Shared Library 在 CentOS 上构建共享库，默认情况下，分布式 GPU 训练是关闭的，仅仅只有一个 GPU 将被使用，为开启分布式 GPU 训练，用 CMake 构建时，设置选项 USE_NCLL=ON ，分布式 GPU 训练依赖 NCLL2 ，可在 https://developer.nvidia.com/nccl 获取