如何提取使用mlr包生成的模型的超级学习器/元学习器系数？

Question

我正在尝试提取makeStackedLearner函数(mlr包)的结果的系数，其中元学习器正在拟合GLM或类似的函数。了解系数对于了解哪些单个模型对最终预测的贡献最大非常有用。

我已经通过mlr github issues (https://github.com/mlr-org/mlr/issues/2598)提出了这个问题。

library(mlr)
data(BostonHousing, package = "mlbench")
tsk = makeRegrTask(data = BostonHousing, target = "medv")
base = c("regr.rpart", "regr.svm")
lrns = lapply(base, makeLearner)
m = makeStackedLearner(base.learners = lrns,
predict.type = "response", method = "compress")
tmp = train(m, tsk)

哪里可以找到super.model的回归系数？

> names(tmp$learner.model)
[1] "method"        "base.learners" "super.model"   "pred.train" 

Answer 1

多亏了mlr包开发人员，这个问题现在已经得到了解决。最简单的做法是使用super.learner = "regr.glm"设置，以便可以轻松解释最终的超级学习器模型：

> data(BostonHousing, package = "mlbench")
> tsk = makeRegrTask(data = BostonHousing, target = "medv")
> BostonHousing$chas = as.numeric(BostonHousing$chas)
> base = c("regr.rpart", "regr.svm", "regr.ranger")
> lrns = lapply(base, makeLearner)
> m = makeStackedLearner(base.learners = lrns,
+                        predict.type = "response", method = "stack.cv", super.learner = "regr.glm")
> tmp = train(m, tsk)
> tmp$learner.model$super.model$learner.model

Call:  stats::glm(formula = f, family = family, data = d, control = ctrl, 
    model = FALSE)

Coefficients:
(Intercept)   regr.rpart     regr.svm  regr.ranger  
   -2.49071     -0.05411      0.28542      0.88404  

Degrees of Freedom: 505 Total (i.e. Null);  502 Residual
Null Deviance:      42720 
Residual Deviance: 5506     AIC: 2654