如何使用嵌套超参数优化在mlr3中测试我们的模型

Question

我刚刚开始学习mlr3，并且已经读过mlr3的书(参数优化)。在书中，他们提供了一个嵌套超参数的例子，但我不知道如何提供最终的预测，即预测(模型，测试数据)。以下代码提供了learner、task、内部重采样(holdout)、外部重采样(3倍CV)和网格搜索来进行调整。我的问题是：

(1) Dont we need to train the optimized model i.e. at in this case like train(at, task) ?

(2) After train, how to predict the data with test data as I am not seeing any splits of train and test data?

The code taken from mlr3 book (https://mlr3book.mlr-org.com/nested-resampling.html) is as follows:  

library("mlr3tuning")
task = tsk("iris")
learner = lrn("classif.rpart")
resampling = rsmp("holdout")
measure = msr("classif.ce")
param_set = paradox::ParamSet$new(
  params = list(paradox::ParamDbl$new("cp", lower = 0.001, upper = 0.1)))
terminator = trm("evals", n_evals = 5)
tuner = tnr("grid_search", resolution = 10)

at = AutoTuner$new(learner, resampling, measure = measure,
  param_set, terminator, tuner = tuner)

rr = resample(task = task, learner = at, resampling = resampling_outer)

Answer 1

参见The "Cross-Validation - Train/Predict" misunderstanding。