Machine learning LightGBM中的装袋工作原理
在lightGBM模型中,有两个参数与装袋相关Machine learning LightGBM中的装袋工作原理,machine-learning,xgboost,lightgbm,Machine Learning,Xgboost,Lightgbm,在lightGBM模型中,有两个参数与装袋相关 bagging_fraction bagging_freq (frequency for bagging 0 means disable bagging; k means perform bagging at every k iteration Note: to enable bagging, bagging_fraction should be set to
bagging_fraction
bagging_freq (frequency for bagging
0 means disable bagging; k means perform bagging at every k
iteration
Note: to enable bagging, bagging_fraction should be set to
value smaller than 1.0 as well)
我可以在gdbt中找到关于这个打包功能的更详细的解释。那么有人给我更详细的解释吗?代码执行文档中所说的内容-它对大小为
bagging\u fraction*N\u train\u示例的训练示例子集进行采样。并且对该子集执行第i树的训练。可以对每棵树(即每一次迭代)或在每棵bagging\u freq
树进行训练后进行取样
例如,bagging\u fraction=0.5,bagging\u freq=10
意味着新的0.5*N\u train\u示例的采样将每10次迭代进行一次