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stacking notes #20
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没有要求。
最好不。参考华泰证券的研究报告,我回头发你看下。
正常的判断,训练集和测试集的评价指标
至少kfold的交叉验证。 |
模型原理、样本分离都是可以处理的方式,更直接的是在 stacking 的时候使用,证明各个预测值相关性低。 林晓明, 陈烨, and 李子钰. 2018. 人工智能选股之stacking集成学习. 华泰证券股份有限公司. 具体地见,https://jiaxiangbu.github.io/phoenix-finance/output/fcontest_output30.html |
https://jiaxiangbu.github.io/learn_fe/target_encoding_learning_notes.html#%E6%80%BB%E7%BB%93 |
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https://jiaxiangbu.github.io/learn_fe/target_encoding_learning_notes.html#%E6%80%BB%E7%BB%93 |
@Ricardo627721141 上次跟你说的 stacking 处理方式有些出入,正确的理解是 |
10.2 stacking
https://jiaxiangbu.github.io/learn_kaggle/learning_notes.html
在训练阶段,
第一层模型,只要保证一个样本不被两次层训练即可The text was updated successfully, but these errors were encountered: