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.
ResampleResults can be visualized via mlr3viz's autoplot() function.
S3 Methods
as.data.table(rr, reassemble_learners = TRUE, convert_predictions = TRUE, predict_sets = "test")
ResampleResult ->data.table::data.table()
Returns a tabular view of the internal data.c(...)
(ResampleResult, ...) -> BenchmarkResult
Combines multiple objects convertible to BenchmarkResult into a new BenchmarkResult.
See also
as_benchmark_result()to convert to a BenchmarkResult.Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter3/evaluation_and_benchmarking.html#sec-resampling
Package mlr3viz for some generic visualizations.
Other resample:
resample()
Active bindings
task_type(
character(1))
Task type of objects in theResampleResult, e.g."classif"or"regr". This isNAfor empty ResampleResults.uhash(
character(1))
Unique hash for this object.iters(
integer(1))
Number of resampling iterations stored in theResampleResult.task(Task)
The taskresample()operated on.learner(Learner)
Learner prototyperesample()operated on. For a list of trained learners, see methods$learners().resampling(Resampling)
Instantiated Resampling object which stores the splits into training and test.learners(list of Learner)
List of trained learners, sorted by resampling iteration.warnings(
data.table::data.table())
A table with all warning messages. Column names are"iteration"and"msg". Note that there can be multiple rows per resampling iteration if multiple warnings have been recorded.errors(
data.table::data.table())
A table with all error messages. Column names are"iteration"and"msg". Note that there can be multiple rows per resampling iteration if multiple errors have been recorded.
Methods
Method new()
Creates a new instance of this R6 class.
An alternative construction method is provided by as_resample_result().
Usage
ResampleResult$new(data = ResultData$new(), view = NULL)Arguments
data(ResultData |
data.table())
An object of type ResultData, either extracted from another ResampleResult, another BenchmarkResult, or manually constructed withas_result_data().view(
character())
Singleuhashof the ResultData to operate on. Used internally for optimizations.
Method help()
Opens the corresponding help page referenced by field $man.
Method prediction()
Combined Prediction of all individual resampling iterations, and all provided predict sets. Note that, per default, most performance measures do not operate on this object directly, but instead on the prediction objects from the resampling iterations separately, and then combine the performance scores with the aggregate function of the respective Measure (macro averaging).
If you calculate the performance on this prediction object directly, this is called micro averaging.
Arguments
predict_sets(
character())
Subset of{"train", "test"}.
Returns
Prediction or empty list() if no predictions are available.
Method predictions()
List of prediction objects, sorted by resampling iteration. If multiple sets are given, these are combined to a single one for each iteration.
If you evaluate the performance on all of the returned prediction objects and then average them, this
is called macro averaging. For micro averaging, operate on the combined prediction object as returned by
$prediction().
Arguments
predict_sets(
character())
Subset of{"train", "test", "internal_valid"}.
Returns
List of Prediction objects, one per element in predict_sets.
Or list of empty list()s if no predictions are available.
Method score()
Returns a table with one row for each resampling iteration, including all involved objects:
Task, Learner, Resampling, iteration number (integer(1)), and (if enabled)
one Prediction for each predict set of the Learner.
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().
Arguments
measuresids(
logical(1))
IfidsisTRUE, 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.conditions(
logical(1))
Adds condition messages ("warnings","errors") as extra list columns of character vectors to the returned tablepredictions(
logical(1))
Additionally return prediction objects, one column for eachpredict_setof the learner. Columns are named"prediction_train","prediction_test"and"prediction_internal_valid", if present.
Method obs_loss()
Calculates the observation-wise loss via the loss function set in the
Measure's field obs_loss.
Returns a data.table() with the columns of the matching Prediction object plus
one additional numeric column for each measure, named with the respective measure id.
If there is no observation-wise loss function for the measure, the column is filled with
NA values.
Note that some measures such as RMSE, do have an $obs_loss, but they require an
additional transformation after aggregation, in this example taking the square-root.
Arguments
measurespredict_sets(
character())
The predict sets.
Method aggregate()
Calculates and aggregates performance values for all provided measures, according to the
respective aggregation function in Measure.
If measures is NULL, measures defaults to the return value of default_measures().
Returns
Named numeric().
Method filter()
Subsets the ResampleResult, reducing it to only keep the iterations specified in iters.
Arguments
iters(
integer())
Resampling iterations to keep.
Method discard()
Shrinks the ResampleResult by discarding parts of the internally stored data. Note that certain operations might stop work, e.g. extracting importance values from learners or calculating measures requiring the task's data.
Arguments
backends(
logical(1))
IfTRUE, the DataBackend is removed from all stored Tasks.models(
logical(1))
IfTRUE, the stored model is removed from all Learners.
Method marshal()
Marshals all stored models.
Arguments
...(any)
Additional arguments passed tomarshal_model().
Method unmarshal()
Unmarshals all stored models.
Arguments
...(any)
Additional arguments passed tounmarshal_model().
Examples
task = tsk("penguins")
learner = lrn("classif.rpart")
resampling = rsmp("cv", folds = 3)
rr = resample(task, learner, resampling)
print(rr)
#> <ResampleResult> with 3 resampling iterations
#> task_id learner_id resampling_id iteration prediction_test warnings
#> penguins classif.rpart cv 1 <PredictionClassif> 0
#> penguins classif.rpart cv 2 <PredictionClassif> 0
#> penguins classif.rpart cv 3 <PredictionClassif> 0
#> errors
#> 0
#> 0
#> 0
# combined predictions and predictions for each fold separately
rr$prediction()
#> <PredictionClassif> for 344 observations:
#> row_ids truth response
#> 2 Adelie Adelie
#> 9 Adelie Adelie
#> 13 Adelie Adelie
#> --- --- ---
#> 337 Chinstrap Gentoo
#> 340 Chinstrap Gentoo
#> 342 Chinstrap Chinstrap
rr$predictions()
#> [[1]]
#> <PredictionClassif> for 115 observations:
#> row_ids truth response
#> 2 Adelie Adelie
#> 9 Adelie Adelie
#> 13 Adelie Adelie
#> --- --- ---
#> 321 Chinstrap Chinstrap
#> 334 Chinstrap Chinstrap
#> 335 Chinstrap Chinstrap
#>
#> [[2]]
#> <PredictionClassif> for 115 observations:
#> row_ids truth response
#> 3 Adelie Adelie
#> 5 Adelie Adelie
#> 6 Adelie Adelie
#> --- --- ---
#> 341 Chinstrap Adelie
#> 343 Chinstrap Gentoo
#> 344 Chinstrap Chinstrap
#>
#> [[3]]
#> <PredictionClassif> for 114 observations:
#> row_ids truth response
#> 1 Adelie Adelie
#> 4 Adelie Adelie
#> 15 Adelie Adelie
#> --- --- ---
#> 337 Chinstrap Gentoo
#> 340 Chinstrap Gentoo
#> 342 Chinstrap Chinstrap
#>
# folds scored separately, then aggregated (macro)
rr$aggregate(msr("classif.acc"))
#> classif.acc
#> 0.9273837
# predictions first combined, then scored (micro)
rr$prediction()$score(msr("classif.acc"))
#> classif.acc
#> 0.9273256
# check for warnings and errors
rr$warnings
#> Empty data.table (0 rows and 2 cols): iteration,msg
rr$errors
#> Empty data.table (0 rows and 2 cols): iteration,msg