resample()This is the result container object returned by resample().
Note that all stored objects are accessed by reference. Do not modify any object without cloning it first.
as.data.table(rr)
ResampleResult -> data.table::data.table()
Returns a copy of the internal data.
c(...)
(ResampleResult, ...) -> BenchmarkResult
Combines multiple objects convertible to BenchmarkResult into a new BenchmarkResult.
data(data.table::data.table())
Internal data storage.
We discourage users to directly work with this field.
task(Task)
The task resample() operated on.
learners(list of Learner) List of trained learners, sorted by resampling iteration.
resampling(Resampling) Instantiated Resampling object which stores the splits into training and test.
uhash(character(1))
Unique hash for this object.
warnings(data.table::data.table())
A table with all warning messages.
Column names are "iteration" and "msg".
Note that there can be multiple rows per resampling iteration if multiple warnings have been recorded.
errors(data.table::data.table())
A table with all error messages.
Column names are "iteration" and "msg".
Note that there can be multiple rows per resampling iteration if multiple errors have been recorded.
new()Creates a new instance of this R6 class.
ResampleResult$new(data, uhash = NULL)
data(data.table::data.table())
Table with data for one resampling iteration per row:
Task, Learner, Resampling, iteration (integer(1)), and Prediction.
uhash(character(1))
Unique hash for this ResampleResult. If NULL, a new unique hash is generated.
This unique hash is primarily needed to group information in BenchmarkResults.
format()Helper for print outputs.
ResampleResult$format()
print()Printer.
ResampleResult$print()
...(ignored).
help()Opens the corresponding help page referenced by field $man.
ResampleResult$help()
prediction()Combined Prediction of all individual resampling iterations, and all provided predict sets. Note that performance measures do not operate on this object, but instead on each prediction object separately and then combine the performance scores with the aggregate function of the respective Measure.
ResampleResult$prediction(predict_sets = "test")
predict_sets(character())
Subset of {"train", "test"}.
predictions()List of prediction objects, sorted by resampling iteration. If multiple sets are given, these are combined to a single one for each iteration.
ResampleResult$predictions(predict_sets = "test")
predict_sets(character())
Subset of {"train", "test"}.
List of Prediction objects, one per element in predict_sets.
score()Returns a table with one row for each resampling iteration, including all involved objects:
Task, Learner, Resampling, iteration number (integer(1)), and Prediction.
Additionally, a column with the individual (per resampling iteration) performance is added for each Measure in measures,
named with the id of the respective measure id.
If measures is NULL, measures defaults to the return value of default_measures().
ResampleResult$score(measures = NULL, ids = TRUE)
ids(logical(1))
If ids is TRUE, extra columns with the ids of objects ("task_id", "learner_id", "resampling_id") are added to the returned table.
These allow to subset more conveniently.
aggregate()Calculates and aggregates performance values for all provided measures, according to the respective aggregation function in Measure.
If measures is NULL, measures defaults to the return value of default_measures().
ResampleResult$aggregate(measures = NULL)
Named numeric().
filter()Subsets the ResampleResult, reducing it to only keep the iterations specified in iters.
ResampleResult$filter(iters)
iters(integer())
Resampling iterations to keep.
Returns the object itself, but modified by reference.
You need to explicitly $clone() the object beforehand if you want to keeps
the object in its previous state.
clone()The objects of this class are cloneable with this method.
ResampleResult$clone(deep = FALSE)
deepWhether to make a deep clone.
# NOT RUN {
task = tsk("iris")
learner = lrn("classif.rpart")
resampling = rsmp("cv", folds = 3)
rr = resample(task, learner, resampling)
print(rr)
rr$aggregate(msr("classif.acc"))
rr$prediction()
rr$prediction()$confusion
rr$warnings
rr$errors
# }
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