Multi-task LightGBM安装

原创 已于 2022-11-13 15:01:25 修改 · 1.3k 阅读 · 2 ·
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于 2022-11-13 13:32:16 首次发布

Multi-task LightGBM库(lightgbmmt)安装

Multi-task LightGBM库(lightgbmmt)安装

本文提供多任务机器学习 (MTML)库中的MT-LightGBM安装方式。

之前使用了阿里大佬开源的树模型多任务机器学习库，基于LightGBM改造的lightgbmmt包，不论是在Mac还是windows上安装都十分费劲，主要是C++相关编译非常麻烦。
最后索性在Docker上安装成功了，现把相关的依赖推送在镜像仓库，供有同样需求的小伙伴直接拉取使用。
镜像名:gianthb/mtgbm
使用 docker pull gianthb/mtgbm:latest
即可拉取成功
原代码仓连接：

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LightGBM安装与配置完全指南 【免费下载链接】LightGBM microsoft/LightGBM: LightGBM 是微软开发的一款梯度提升机（Gradient Boosting Machine, GBM）框架，具有高效、分布式和并行化等特点，常用于机器学习领域的分类和回归任务，在数据科学竞赛和工业界有广泛应...
:\pycharm\代码\.venv\Scripts\python.exe E:\pycharm\代码\代码调试9.py ⚠️ skopt未安装！贝叶斯优化不可用。请运行: pip install scikit-optimize ⚠️ shap未安装！SHAP可解释性不可用。请运行: pip install shap ========================================================================================== 【增强版机器学习分析系统】 基于张继权教授‘四因子理论’：致灾因子、孕灾环境、承灾体、防灾减灾能力 功能：自动区分分类/回归 + 多模型 + 缺失值修复 + 结果可视化 ========================================================================================== 📁 数据路径: E:\pycharm\meta\整合数据.csv 🎯 目标变量: PFOA 1. 加载数据... ❌ 编码 'utf-8' 解码失败，尝试下一个... ✅ 数据加载成功！编码: gbk | 形状: (137, 111) 🧹 已删除 96 个 'Unnamed' 列。 2.1 应用张继权教授四因子理论进行特征分组... 【四因子特征分组表】 风险因子 特征数量 特征列表 致灾因子 2 PFBA；PFOS 孕灾环境 2 经度；纬度 承灾体 2 城市；作物类型 防灾减灾能力 0 无 其他 8 样本编号；PFPeA；PFHxA；PFHpA；PFNA；PFDA；PFBS；PFHxS 2. 探索性数据分析（EDA）... 统计项 值 样本数 137 特征数 14 目标变量 PFOA 唯一值数 111 🔍 检测到连续型目标变量 'PFOA'（唯一值占比0.81），切换为回归任务。 3. 数据预处理与标准化... 🧹 删除 12 行因 'PFOA' 缺失 (NaN) 训练集: (100, 14), 测试集: (25, 14) 4. 初始化 回归 模型... ✅ 已加载 5 个回归模型 5. 模型训练与评分评估... ➜ 训练 线性回归... ➜ 训练 随机森林回归... ➜ 训练 XGBoost回归... ➜ 训练 LightGBM回归... [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000047 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 263 [LightGBM] [Info] Number of data points in the train set: 100, number of used features: 11 [LightGBM] [Info] Start training from score 2.342153 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf File "E:\pycharm\代码\.venv\lib\site-packages\joblib\externals\loky\backend\context.py", line 257, in _count_physical_cores cpu_info = subprocess.run( File "C:\Users\hp\AppData\Local\Programs\Python\Python38\lib\subprocess.py", line 493, in run with Popen(*popenargs, **kwargs) as process: File "C:\Users\hp\AppData\Local\Programs\Python\Python38\lib\subprocess.py", line 858, in __init__ self._execute_child(args, executable, preexec_fn, close_fds, File "C:\Users\hp\AppData\Local\Programs\Python\Python38\lib\subprocess.py", line 1311, in _execute_child hp, ht, pid, tid = _winapi.CreateProcess(executable, args, [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf ➜ 训练 CatBoost回归... 【回归模型性能对比表】 r2 mse rmse 模型 XGBoost回归 0.878321 0.074486 0.272921 随机森林回归 0.74793 0.154304 0.392816 CatBoost回归 0.416065 0.357455 0.597875 线性回归 -13.316012 8.763525 2.960325 LightGBM回归 -28.162328 17.851676 4.225124 🏆 最佳模型: XGBoost回归 (R²: 0.8783) ========================================================================================== ✅ 完整机器学习分析流程执行完毕！ ========================================================================================== 进程已结束，退出代码为 0 继续解决报错，代码中加入相关性分析等，生成更多的图
11-12
from lightgbm import LGBMRegressor from sklearn.linear_model import LinearRegression from sklearn.ensemble import RandomForestRegressor print("=" * 90) print("【专业级机器学习分析系统】") print(...
E:\pycharm\代码\.venv\Scripts\python.exe E:\pycharm\代码\代码调试9.py ⚠️ skopt未安装！贝叶斯优化不可用。请运行: pip install scikit-optimize ⚠️ shap未安装！SHAP可解释性不可用。请运行: pip install shap ========================================================================================== 【增强版机器学习分析系统】 基于张继权教授‘四因子理论’：致灾因子、孕灾环境、承灾体、防灾减灾能力 功能：自动区分分类/回归 + 多模型 + PCA降维 + 贝叶斯超参优化 + SHAP可解释性 + 中文可视化 ========================================================================================== 📁 数据路径: E:\pycharm\meta\整合数据.csv 🎯 目标变量: PFOA 1. 加载数据... ❌ 编码 'utf-8' 解码失败，尝试下一个... ✅ 数据加载成功！编码: gbk | 形状: (137, 111) 🧹 已删除 96 个 'Unnamed' 列。 2.1 应用张继权教授四因子理论进行特征分组... 【四因子特征分组表】 风险因子 特征数量 特征列表 致灾因子 2 PFBA；PFOS 孕灾环境 2 经度；纬度 承灾体 2 城市；作物类型 防灾减灾能力 0 无 其他 8 样本编号；PFPeA；PFHxA；PFHpA；PFNA；PFDA；PFBS；PFHxS 2. 探索性数据分析（EDA）... 数据基本信息: 统计项 值 样本数 137 特征数 14 目标变量 PFOA 类别数/唯一值 111 🔍 检测到目标变量 'PFOA' 为连续型变量（唯一值占比0.81），切换为回归任务。 3. 数据预处理与标准化... 🧹 检测到 21 个异常值，已限制在合理范围。 训练集: (109, 14), 测试集: (28, 14) 4. 初始化 回归 模型... ✅ 已加载 10 个回归模型 5. 模型训练与评分评估... ➜ 训练 线性回归... ❌ 线性回归 失败: Input y contains NaN. ➜ 训练 决策树回归... ❌ 决策树回归 失败: Input y contains NaN. ➜ 训练 随机森林回归... ❌ 随机森林回归 失败: Input y contains NaN. ➜ 训练 梯度提升回归... ❌ 梯度提升回归 失败: Input y contains NaN. ➜ 训练 XGBoost回归... ❌ XGBoost回归 失败: [16:00:55] C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-08cbc0333d8d4aae1-1\xgboost\xgboost-ci-windows\src\data\data.cc:514: Check failed: valid: Label contains NaN, infinity or a value too large. ➜ 训练 LightGBM回归... File "E:\pycharm\代码\.venv\lib\site-packages\joblib\externals\loky\backend\context.py", line 257, in _count_physical_cores cpu_info = subprocess.run( File "C:\Users\hp\AppData\Local\Programs\Python\Python38\lib\subprocess.py", line 493, in run with Popen(*popenargs, **kwargs) as process: File "C:\Users\hp\AppData\Local\Programs\Python\Python38\lib\subprocess.py", line 858, in __init__ self._execute_child(args, executable, preexec_fn, close_fds, File "C:\Users\hp\AppData\Local\Programs\Python\Python38\lib\subprocess.py", line 1311, in _execute_child hp, ht, pid, tid = _winapi.CreateProcess(executable, args, [LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000098 seconds. You can set `force_col_wise=true` to remove the overhead. [LightGBM] [Info] Total Bins 270 [LightGBM] [Info] Number of data points in the train set: 109, number of used features: 11 [LightGBM] [Info] Start training from score 0.152646 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Warning] No further splits with positive gain, best gain: -inf ❌ LightGBM回归 失败: Input contains NaN. ➜ 训练 KNN回归... ❌ KNN回归 失败: Input y contains NaN. ➜ 训练 SVR... ❌ SVR 失败: Input y contains NaN. ➜ 训练 MLP回归... ❌ MLP回归 失败: Input y contains NaN. ➜ 训练 CatBoost回归... ❌ CatBoost回归 失败: catboost/libs/metrics/metric.cpp:6956: metric/loss-function RMSE do not allows nan value on target Traceback (most recent call last): File "E:\pycharm\代码\代码调试9.py", line 390, in <module> pipeline.run_complete_pipeline() File "E:\pycharm\代码\代码调试9.py", line 376, in run_complete_pipeline self.train_and_evaluate() File "E:\pycharm\代码\代码调试9.py", line 350, in train_and_evaluate results_df.sort_values('r2', ascending=False, inplace=True) File "E:\pycharm\代码\.venv\lib\site-packages\pandas\core\frame.py", line 6758, in sort_values k = self._get_label_or_level_values(by, axis=axis) File "E:\pycharm\代码\.venv\lib\site-packages\pandas\core\generic.py", line 1778, in _get_label_or_level_values raise KeyError(key) KeyError: 'r2' 进程已结束，退出代码为 1 继续解决报错并安装未安装的代码，生成完整代码
11-12
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在进行节点的分裂时，需要计算每个特征的增益，最终选增益最大的那个特征去做分裂，那么各个特征的增益计算就可以开多线程进行。然后我们拿残差替代A,B,C,D的原值，到第二棵树去学习，如果我们的预测值和它们的残差相等，则只需把第二棵树的结论累加到第一棵树上就能得到真实年龄了。从树深度0开始，每一节点都遍历所有的特征，比如年龄、性别等等，然后对于某个特征，先按照该特征里的值进行排序，然后线性扫描该特征进而确定最好的分割点，最后对所有特征进行分割后，我们选择所谓的增益Gain最高的那个特征。
LightGBM 0.1编译后的包
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LightGBM win10下亲测可用，0.1版本，附最新版本的LightGBM python版安装方法
lightgbm，一个超强的 Python 库！
涛哥聊Python
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更多Python学习内容：ipengtao.com大家好，今天为大家分享一个超强的 Python 库 - pyramid。Github地址：https://github.com/Pylons/pyramidLightGBM是一个快速、分布式、高性能的梯度提升决策树（Gradient Boosting Decision Tree）库，它在机器学习和数据挖掘领域被广泛应用。本文将介绍LightGBM库...
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weixin_30205153的博客
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在本章节中，您学习了如何在Windows 10上安装LightGBM。首先了解了安装前的系统要求与环境准备，然后逐步讲解了如何下载和安装预编译安装包，并且学习了如何解决安装过程中可能出现的常见问题。有了这些基础知识，您应该可以顺利地安装LightGBM，并开始构建和部署强大的机器学习模型。在下一章，我们将深入探讨使用Python语言集成LightGBM，并给出实际模型训练和预测的示例。预编译安装包通常包含了编译好的二进制文件、依赖库、文档以及示例脚本等。
lightgbm安装
qq_44778404的博客
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直接cmd，用pip install lightgbm即可
在Windows下安装LightGBM的Python包
机器变得更残忍的博客
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LightGBM是微软旗下DMTK推出的Gradient Boosting框架，因为其快速高效，以后或许会成为数据挖掘竞赛中的又一个大杀器。1.下载源代码git clone --recursive https://github.com/Microsoft/LightGBM2.编译DLL 进入LightGBM目录，用VS2013（或者更高的版本）打开windows/LightGBM.sln，选择D
lightgbm过去版本安装包_lightgbm GPU版本安装
weixin_27256197的博客
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官网https://lightgbm.readthedocs.io/en/latest/GPU-Windows.htmllightgbm GPU版本 Windows情况下安装：https://www.jianshu.com/p/30555fd2bd50以下基于ubuntu 16.04 python 3.6.5安装测试成功1、安装软件依赖sudo apt-get install --no-inst...
LightGBM安装教程
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实战五：基于CatBoost,XGBoost,LightGBM的路况多分类任务 代码+数据 可直接运行 详细教程
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