关键词 bagging boosting stacking blending
https://www.kaggle.com/tivfrvqhs5/introduction-to-ensembling-stacking-in-python/notebook
https://github.com/vwvolodya/Iceberg-Classifier-Challenge/blob/master/cnn/stacking.py
深度 | 从Boosting到Stacking,概览集成学习的方法与性能
LR(Logistic Regression) & XGBOOST 学习笔记
ensemble 总结 Kaggle-Ensemble-Guide
https://mlwave.com/kaggle-ensembling-guide/
stacking
Kaggle进阶系列:zillow竞赛特征提取与模型融合(LB~0.644)
http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html
https://github.com/ikki407/stacking
https://github.com/rushter/heamy
https://github.com/lytforgood/MachineLearningTrick
https://github.com/dnkirill/allstate_capstone
https://www.kaggle.com/chenpeikai/stacking-methold
本文介绍了集成学习中的几种关键方法,包括bagging、boosting、stacking和blending,并提供了多个实际应用案例。通过这些方法,可以有效提升模型的准确性和稳定性。
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