模型融合 阳光穿透心脏的1/2处 2022-06-10 12:55 151阅读 0赞 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 [模型融合方法概述][Link 1] [在Caffe中实现模型融合][Caffe] [集成学习-模型融合学习笔记][-] [模型融合(stacking&blending)][stacking_blending] [KAGGLE ENSEMBLING GUIDE][] [集成学习总结 & Stacking方法详解][_ Stacking] [深度 | 从Boosting到Stacking，概览集成学习的方法与性能][_ _Boosting_Stacking] [XGBOOST + LR 模型融合 python 代码][XGBOOST _ LR _ python] [LR(Logistic Regression) & XGBOOST 学习笔记][LR_Logistic Regression_ _ XGBOOST] [模型融合之Bagging,及scikit learning][Bagging_scikit learning] [ｅｎｓｅｍｂｌｅ 总结 Kaggle-Ensemble-Guide][Kaggle-Ensemble-Guide] [https://mlwave.com/kaggle-ensembling-guide/][KAGGLE ENSEMBLING GUIDE] stacking [Kaggle 机器学习之模型融合（stacking）心得][Kaggle _stacking] [Kaggle进阶系列：zillow竞赛特征提取与模型融合（LB~0.644）][Kaggle_zillow_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 # [模型集成 | 14款常规机器学习 + 加权平均模型融合][_ 14_ _] # [随机加权平均 -- 在深度学习中获得最优结果的新方法][--] [Link 1]: https://zhuanlan.zhihu.com/p/25836678 [Caffe]: http://www.cnblogs.com/frombeijingwithlove/p/6683476.html [-]: http://blog.csdn.net/q383700092/article/details/53557410 [stacking_blending]: http://blog.csdn.net/u014465639/article/details/72844371 [KAGGLE ENSEMBLING GUIDE]: https://mlwave.com/kaggle-ensembling-guide/ [_ Stacking]: http://blog.csdn.net/willduan1/article/details/73618677 [_ _Boosting_Stacking]: https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&mid=2650730238&idx=2&sn=18239d7ea90de70c939d704f3f8482d8&chksm=871b2a80b06ca396e3a71fdfdf9574886a776ef99971bf0be4e818c568424c2a337c6e1cd14b&mpshare=1&scene=1&srcid=0830wx42DQTPNlATElGFAxAW&pass_ticket=qTAVmkAknsNqSr1xA%2FZA9hSIkZpIexCcIgCpGGfH41D%2BR5vXZ4qQvltWqP5wCamn#rd [XGBOOST _ LR _ python]: http://blog.csdn.net/jerr__y/article/details/79005842 [LR_Logistic Regression_ _ XGBOOST]: http://blog.csdn.net/jerr__y/article/details/78924485 [Bagging_scikit learning]: http://blog.csdn.net/ZengHaihong/article/details/53247100 [Kaggle-Ensemble-Guide]: http://blog.csdn.net/u014114990/article/details/50819948 [Kaggle _stacking]: https://mp.weixin.qq.com/s?__biz=MjM5ODU3OTIyOA==&mid=2650666954&idx=2&sn=373b83ec854478e59725dd4ad31829ea&chksm=bec1cab989b643af57758c16722350f62317c9226860509dfcdb1c9723d48b22049cbc920c05&mpshare=1&scene=1&srcid=11119pVqOqgKS1LZUaLlXTy4&pass_ticket=%2F1wIxAPrucZXXhfwcnEaaLa17m4OeCaWnkbulXu%2FNJv3j0HmaaAKKFwgIwRe%2FkON#rd [Kaggle_zillow_LB_0.644]: https://zhuanlan.zhihu.com/p/29794651 [_ 14_ _]: https://blog.csdn.net/sinat_26917383/article/details/80905004 [--]: https://mp.weixin.qq.com/s/tjUHr8l-oHn_5lw-tQW4cA
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