xgboost，抵消曝光? [英] xgboost, offset exposure?

问题描述

我正在 R 中建模声明频率(泊松分布).我使用的是 gbm 和 xgboost 包，但似乎 xgboost 没有将曝光考虑在内的偏移参数?

I am modelling a claims frequency (poisson distr) in R. I am using the gbm and xgboost packages, but it seems that xgboost does not have an offset parameter to take the exposure into account?

在 gbm 中，人们会按如下方式考虑曝光:

In a gbm, one would take the exposure into account as follows:

gbm.fit(x = train,y = target, n.trees = 100,distribution = "poisson", offset = log(exposure))

如何使用 `xgboost 实现相同的效果?

How do I achieve the same with `xgboost?

PS:我不能使用曝光作为预测因子，因为每次观察到声明时都会创建一个新的观察.

PS: I cannot use the exposure as predictor since a new obs is created each time a claim is observed.

推荐答案

创建 xgboost 矩阵后，您可以使用 setinfo 和 base_margin 属性设置偏移量，例如:

Once you have created your xgboost matrix you can set an offset using setinfo and the base_margin attribute, eg:

setinfo(xgtrain, "base_margin", log(d$exposure))

你可以从我在这里问的类似问题中看到完整的例子:XGBoost- 具有不同曝光/偏移量的泊松分布

You can see the full example from the similar question I asked here: XGBoost - Poisson distribution with varying exposure / offset

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