将交叉验证与GLM lambda搜索结合使用时，需要注意什么？ [英] What do you need to watch out for when using cross-validation with GLM lambda search?

问题描述

关于 h2o.glm lambda搜索似乎并未遍历所有lambda ，我读到这个问题是因为抱怨lambda是太高;他们尝试设置 early_stopping = F 希望可以解决该错误。

Regarding h2o.glm lambda search not appearing to iterate over all lambdas, I read the question as complaining that lambda was too high; they tried setting early_stopping=F in the hope that might fix that "bug".

不是原来的行为是功能，而不是错误？并且，如果这是正确的，则在与GLM进行交叉验证时，应始终使用 early_stopping = T ，否则交叉验证的错误估计将毫无用处；您也会冒过度拟合的风险。

Isn't it the case that the original behaviour was a feature, not a bug? And if that is correct, then you should always use early_stopping=T when using cross-validation with GLM, otherwise the error estimate from cross-validation is useless; you also risk over-fitting.

（我的主要问题是，我对GLM和CV协同工作方式的理解是否正确；但是如果有

(My main question is if my understanding of the way GLM and CV work together is correct; but I'd be interested if there are any other things to watch out for when using lambda_search and cross-validation together.)

推荐答案

带有lambda搜索和交叉验证的H2O glm，请注意其他所有事项。验证应始终根据交叉验证选择最佳的lambda并将其用于返回的（主）模型中。提前停止选项对选定的lambda无效。其目的是跳过对于lambdas> best的模型的计算，因为主要模型不需要它们（我们仍然为lambdas< best计算模型，因为这允许使用热启动并充分利用强规则）。

H2O's glm with lambda search and cross-validation should always pick the best lambda based on cross-validation and use that in the returned (main) model. The early stopping option should have no effect on selected lambda. Its purpose is to skip computation of models for lambdas > best since they are not needed for the main model (we still compute models for lambdas < best since that allows to use warm starting and take full advantage of strong rules).

我认为将Early_stopping设置为false的行为应该为所有lambda计算模型，以防用户希望看到它们/进行自定义模型选择。

I think the behavior with early_stopping set to false should compute models for all lambdas in case user wants to see them / do custom model selection.

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