This function allows to manually construct a ResampleResult or BenchmarkResult by combining the individual components to an object of class ResultData, mlr3's internal container object. A ResampleResult or BenchmarkResult can then be initialized with the returned object. Note that ResampleResults can be converted to a BenchmarkResult with as_benchmark_result() and multiple BenchmarkResults can be combined to a larger BenchmarkResult.

as_result_data(
  task,
  learners,
  resampling,
  iterations,
  predictions,
  learner_states = NULL
)

Arguments

task

(Task).

learners

(list of trained Learners).

resampling

(Resampling).

iterations

(integer()).

predictions

(list of Predictions).

learner_states

(list())
Learner states. If not provided, the states of learners are automatically extracted.

Value

ResultData object which can be passed to the constructor of ResampleResult.

Examples

task = tsk("iris") learner = lrn("classif.rpart") resampling = rsmp("cv", folds = 2)$instantiate(task) iterations = seq_len(resampling$iters) # manually train two learners. # store learners and predictions learners = list() predictions = list() for (i in iterations) { l = learner$clone(deep = TRUE) learners[[i]] = l$train(task, row_ids = resampling$train_set(i)) predictions[[i]] = l$predict(task, row_ids = resampling$test_set(i)) } rdata = as_result_data(task, learners, resampling, iterations, predictions) ResampleResult$new(rdata)
#> <ResampleResult> of 2 iterations #> * Task: iris #> * Learner: classif.rpart #> * Warnings: 0 in 0 iterations #> * Errors: 0 in 0 iterations