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我对此有一个后续问题。与最初的问题一样，我正在使用 mlr3verse，拥有一个新数据集，并希望使用在自动调整期间表现良好的参数进行预测。该问题的答案是使用 at$train(task)。这似乎又开始了调优。它是否通过使用这些参数完全利用了嵌套重采样？

此外，查看 $tuning_result 有两组参数，一组称为 tune_x，一组称为 params。这些有什么区别？

谢谢。

编辑：下面添加的示例工作流

library(mlr3verse)

set.seed(56624)
task = tsk("mtcars")

learner = lrn("regr.xgboost")

tune_ps = ParamSet$new(list(
  ParamDbl$new("eta", lower = .1, upper = .4),
  ParamInt$new("max_depth", lower = 2, upper = 4)
))

at = AutoTuner$new(learner = learner, 
                   resampling = rsmp("holdout"), # inner resampling
                   measures = msr("regr.mse"), 
                   tune_ps = tune_ps,
                   terminator = term("evals", n_evals = 3),
                   tuner = tnr("random_search"))

rr = resample(task = task, learner = at, resampling = rsmp("cv", folds = 2),
              store_models = TRUE)
rr$aggregate()
rr$score()
lapply(rr$learners, function(x) x$tuning_result)

at$train(task)
at$tuning_result

notreallynew.df = as.data.table(task)
at$predict_newdata(newdata = notreallynew.df)
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1 回答 1

2

如前所述?AutoTuner，该类适合具有在调整期间找到的最佳超参数的模型。然后，此模型用于预测，在您的情况下，在调用其方法时用于 newdata .$predict_newdata()。

您还?AutoTuner可以看到链接到?TuningInstance. 然后，这会告诉您$tune_x和params插槽代表什么。下次尝试查找帮助页面 - 这就是它们的用途；）

这似乎又开始了调优。

为什么又来了？它首先在对 的所有观察中做到这一点task。我假设您可能会对“训练/预测”与“重新采样”之间的常见误解感到困惑。阅读有关两者的理论差异的更多信息，以了解两者在做什么。他们有完全不同的目标并且没有联系。也许下面的表述更清楚了。

library(mlr3verse)
#> Loading required package: mlr3
#> Loading required package: mlr3db
#> Loading required package: mlr3filters
#> Loading required package: mlr3learners
#> Loading required package: mlr3pipelines
#> Loading required package: mlr3tuning
#> Loading required package: mlr3viz
#> Loading required package: paradox

set.seed(56624)
lgr::get_logger("mlr3")$set_threshold("warn")
task = tsk("mtcars")
learner = lrn("regr.xgboost")

tune_ps = ParamSet$new(list(
  ParamDbl$new("eta", lower = .1, upper = .4),
  ParamInt$new("max_depth", lower = 2, upper = 4)
))

at = AutoTuner$new(
  learner = learner,
  resampling = rsmp("holdout"), # inner resampling
  measures = msr("regr.mse"),
  tune_ps = tune_ps,
  terminator = term("evals", n_evals = 3),
  tuner = tnr("random_search"))

# train/predict with AutoTuner
at$train(task)
notreallynew.df = as.data.table(task)
at$predict_newdata(newdata = notreallynew.df)
#> <PredictionRegr> for 32 observations:
#>     row_id truth response
#>          1  21.0 9.272631
#>          2  21.0 9.272631
#>          3  22.8 9.272631
#> ---                      
#>         30  19.7 9.272631
#>         31  15.0 5.875841
#>         32  21.4 9.272631

# resample with AutoTuner for performance estimation

rr = resample(
  task = task, learner = at, resampling = rsmp("cv", folds = 2),
  store_models = TRUE)
rr$aggregate()
#> regr.mse 
#> 240.5866
rr$score()
#>          task task_id     learner         learner_id     resampling
#> 1: <TaskRegr>  mtcars <AutoTuner> regr.xgboost.tuned <ResamplingCV>
#> 2: <TaskRegr>  mtcars <AutoTuner> regr.xgboost.tuned <ResamplingCV>
#>    resampling_id iteration prediction regr.mse
#> 1:            cv         1     <list> 220.8076
#> 2:            cv         2     <list> 260.3656

由reprex 包（v0.3.0）于 2020 年 5 月 7 日创建

于 2020-05-07T10:30:39.620 回答