机器学习与算法源代码10： 机器学习神器：XGBoost&LightGBM模型.zip

[{"title":"（ 30 个子文件 5.17MB ） 机器学习与算法源代码10： 机器学习神器：XGBoost&LightGBM模型.zip","children":[{"title":"机器学习与算法源代码10： 机器学习神器：XGBoost&LightGBM模型","children":[{"title":"源代码汇总_Pycharm","children":[{"title":"陈天奇博士XGBoost论文PPT.pdf <span style='color:#111;'> 1.40MB </span>","children":null,"spread":false},{"title":"10.5 LightGBM算法案例实战1 - 客户违约预测模型.py <span style='color:#111;'> 2.21KB </span>","children":null,"spread":false},{"title":"10.6 LightGBM算法案例实战2 - 广告收益回归预测模型.py <span style='color:#111;'> 1.42KB </span>","children":null,"spread":false},{"title":"10.2 XGBoost算法案例实战1 - 金融反欺诈模型.py <span style='color:#111;'> 2.26KB </span>","children":null,"spread":false},{"title":"陈天奇博士XGBoost论文.pdf <span style='color:#111;'> 922.65KB </span>","children":null,"spread":false},{"title":"10.4 LightGBM算法的简单代码实现.py <span style='color:#111;'> 430B </span>","children":null,"spread":false},{"title":"10.1 XGBoost算法的简单代码实现.py <span style='color:#111;'> 450B </span>","children":null,"spread":false},{"title":"客户信息及违约表现.xlsx <span style='color:#111;'> 43.68KB </span>","children":null,"spread":false},{"title":"10.3.2 XGBoost算法案例实战2 - 信用评分模型.py <span style='color:#111;'> 2.24KB </span>","children":null,"spread":false},{"title":"微软出品LightGBM相关论文.pdf <span style='color:#111;'> 357.77KB </span>","children":null,"spread":false},{"title":"信用卡交易数据.xlsx <span style='color:#111;'> 39.15KB </span>","children":null,"spread":false},{"title":"广告收益数据.xlsx <span style='color:#111;'> 37.72KB </span>","children":null,"spread":false},{"title":"10.3.1 XGBoost算法案例实战2 - 信用评分模型.py <span style='color:#111;'> 1.42KB </span>","children":null,"spread":false},{"title":"信用评分卡模型.xlsx <span style='color:#111;'> 45.16KB </span>","children":null,"spread":false}],"spread":false},{"title":"源代码汇总_Jupyer Notebook","children":[{"title":"陈天奇博士XGBoost论文PPT.pdf <span style='color:#111;'> 1.40MB </span>","children":null,"spread":false},{"title":"10.5 LightGBM算法案例实战1 - 客户违约预测模型.ipynb <span style='color:#111;'> 23.81KB </span>","children":null,"spread":false},{"title":"10.4.3 LightGBM算法的简单代码实现.ipynb <span style='color:#111;'> 3.82KB </span>","children":null,"spread":false},{"title":"陈天奇博士XGBoost论文.pdf <span style='color:#111;'> 922.65KB </span>","children":null,"spread":false},{"title":"客户信息及违约表现.xlsx <span style='color:#111;'> 43.68KB </span>","children":null,"spread":false},{"title":"10.6 LightGBM算法案例实战2 - 广告收益回归预测模型.ipynb <span style='color:#111;'> 10.67KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"10.3 XGBoost算法案例实战2 - 信用评分模型-checkpoint.ipynb <span style='color:#111;'> 72B </span>","children":null,"spread":false},{"title":"10.4.3 LightGBM算法的简单代码实现-checkpoint.ipynb <span style='color:#111;'> 3.82KB </span>","children":null,"spread":false},{"title":"10.5 LightGBM算法案例实战1 - 客户违约预测模型-checkpoint.ipynb <span style='color:#111;'> 29.28KB </span>","children":null,"spread":false},{"title":"10.2 XGBoost算法案例实战1 - 金融反欺诈模型-checkpoint.ipynb <span style='color:#111;'> 72B </span>","children":null,"spread":false},{"title":"10.6 LightGBM算法案例实战2 - 广告收益回归预测模型-checkpoint.ipynb <span style='color:#111;'> 10.46KB </span>","children":null,"spread":false},{"title":"10.1.3 XGBoost算法的简单代码实现-checkpoint.ipynb <span style='color:#111;'> 72B </span>","children":null,"spread":false}],"spread":true},{"title":"微软出品LightGBM相关论文.pdf <span style='color:#111;'> 357.77KB </span>","children":null,"spread":false},{"title":"信用卡交易数据.xlsx <span style='color:#111;'> 39.15KB </span>","children":null,"spread":false},{"title":"广告收益数据.xlsx <span style='color:#111;'> 37.72KB </span>","children":null,"spread":false},{"title":"信用评分卡模型.xlsx <span style='color:#111;'> 45.16KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}],"spread":true}]