软件包h2oEnsemble中的h2o.ensemble和h2o.stack有什么区别 [英] what is the different between h2o.ensemble and h2o.stack in package h2oEnsemble

问题描述

根据功能说明进行编码:

Accoding to the Description of function:

h2o.stack::此功能使用用户指定的现有H2O基本模型列表创建超级学习者"(堆叠)集合.

h2o.stack: This function creates a "Super Learner" (stacking) ensemble using a list of existing H2O base models specified by the user.

h2o.ensemble::此功能使用用户指定的H2O基础学习算法创建超级学习者"(堆叠)集成.

h2o.ensemble: This function creates a "Super Learner" (stacking) ensemble using the H2O base learning algorithms specified by the user.

推荐答案

它们是构造整体的两种不同方法.它们具有不同的接口，但是最终它们会产生完全相同类型的对象.

They are two different ways to construct an ensemble. They have a different interface, but they produce the exact same type of object in the end.

• h2o.stack()函数将已经训练(并且经过交叉验证)的H2O模型的列表作为输入，因此它所要做的就是快速的金属化(合并)步骤.如果要使用H2O模型的网格或H2O模型的网格的集合作为基础学习者，这将很有用.唯一的警告是，所有基础学习者必须使用相同的交叉验证折叠.如果您在所有基础学习器(或网格)中使用fold_assignment = "Modulo"，以确保折叠相同.
• h2o.ensemble()函数允许用户在集合中指定所需的基本模型，然后对基本模型进行所有训练和交叉验证，然后也执行金属收益(合并)步骤.这需要更长的时间，因为它还必须训练所有基本模型.

• The h2o.stack() function takes as input a list of already trained (and cross-validated) H2O models, so all it needs to do is the metalearning (combiner) step, which is very fast. This is useful if you want to use a grid of H2O models or a collection of grids of H2O models as the base learners. The only caveat is that all the base learners must have used identical cross-validation folds. If you use fold_assignment = "Modulo" in all the base learners (or grid) that will ensure identical folds.
• The h2o.ensemble() function allows the user to specify which base models they want in the ensemble and then does the all of the training and cross-validation of the base models, and then does the metalearning (combiner) step as well. This takes much longer since it has to train all the base models as well.

从最新的稳定发行版(H2O 3.10.3.*)开始，现在可以在H2O(R，Python，Java，Scala)中以"Stacked Ensemble"方法以本机方式进行堆叠.有关的更多信息，请此处 .但是， h2oEnsemble R程序包(其中和h2o.stack()实时功能).

As of the latest stable release (H2O 3.10.3.*), stacking is now available natively in H2O (R, Python, Java, Scala) as the "Stacked Ensemble" method. More info on that here. However, the h2oEnsemble R package (where the h2o.ensemble() and h2o.stack() functions live) will continue to be supported as well.

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