如何确定哪一种褶皱最终被用作简历中的测试？

Question

如何确定在mlr包中哪一种折叠最终被用作测试，哪种折叠作为5倍交叉验证中的培训？方法$resampling$train.inds和$resampling$test.inds在没有最终达到训练和测试目的的信息的情况下返回全部5倍。

library("mlr")

regr_task = makeRegrTask(data = mtcars, target = "hp")
learner = makeLearner("regr.ranger", 
                      importance = "impurity", 
                      num.threads = 3)
par_set = makeParamSet(
   makeIntegerParam("num.trees", lower = 100L, upper = 500L),
   makeIntegerParam("mtry", lower = 4L, upper = 8L)
)
rdesc = makeResampleDesc("CV", iters = 5, predict = "both")
meas = rmse
ctrl = makeTuneControlGrid()
set.seed(1)
tuned_model = tuneParams(learner = learner,
                         task = regr_task,
                         resampling = rdesc,
                         measures = list(meas, setAggregation(meas, train.mean)),
                         par.set = par_set,
                         control = ctrl,
                         show.info = FALSE)
tuned_model
model_rf = setHyperPars(learner = learner, par.vals = tuned_model$x)
set.seed(1)
model_rf = train(learner = model_rf, task = regr_task)
model_rf

tuned_model$resampling$train.inds
tuned_model$resampling$test.inds

Answer 1

你把事情搞混了。

你要把你的数据分成5倍。每一层都由训练和测试数据组成。这就是为什么您要为$resampling$train.inds和$resampling$test.inds返回一个5的列表。如果你分裂成5倍，你将训练在4个分区(80%的数据)和评估在一个分区(20%的数据)。

正确的措辞是：“哪种指数用于培训和测试？”下面的代码回答了这个问题。

tuned_model$resampling$train.inds
[[1]]
 [1] 10 32  6 15 20 28 26 12  8 24 31 27 22  2 13 29 17 11  1  3 16 18 21 19  9  5

[[2]]
 [1] 10  6 15 28 26 12 23 30  8 25 24  7 31 27 14  2 13 29 17  1 16  4 21 19  9

[[3]]
 [1] 10 32 20 26 12 23 30  8 25  7 27 22 14  2 13 29 17 11  1  3 16 18  4 19  5

[[4]]
 [1] 32  6 15 20 28 26 12 23 30 25 24  7 31 22 14 13 17 11  1  3 18  4 21 19  9  5

[[5]]
 [1] 10 32  6 15 20 28 23 30  8 25 24  7 31 27 22 14  2 29 11  3 16 18  4 21  9  5

> tuned_model$resampling$test.inds
[[1]]
[1]  4  7 14 23 25 30

[[2]]
[1]  3  5 11 18 20 22 32

[[3]]
[1]  6  9 15 21 24 28 31

[[4]]
[1]  2  8 10 16 27 29

[[5]]
[1]  1 12 13 17 19 26