BenchmarkResult: Container for Benchmarking Results
In mlr-org/mlr3: Machine Learning in R - Next Generation
BenchmarkResult R Documentation
Container for Benchmarking Results
Description
This is the result container object returned by benchmark().
A BenchmarkResult consists of the data of multiple ResampleResults.
The contents of a BenchmarkResult and ResampleResult are almost identical and the stored ResampleResults can be extracted via the $resample_result(i) method, where i is the index of the performed resample experiment.
This allows us to investigate the extracted ResampleResult and individual resampling iterations, as well as the predictions and models from each fold.
BenchmarkResults can be visualized via mlr3viz's autoplot() function.
For statistical analysis of benchmark results and more advanced plots, see mlr3benchmark.
S3 Methods
-
as.data.table(rr, ..., reassemble_learners = TRUE, convert_predictions = TRUE, predict_sets = "test", task_characteristics = FALSE)
BenchmarkResult -> data.table::data.table()
Returns a tabular view of the internal data.
-
c(...)
(BenchmarkResult, ...) -> BenchmarkResult
Combines multiple objects convertible to BenchmarkResult into a new BenchmarkResult.
Active bindings
task_type(character(1))
Task type of objects in the BenchmarkResult.
All stored objects (Task, Learner, Prediction) in a single BenchmarkResult are
required to have the same task type, e.g., "classif" or "regr".
This is NA for empty BenchmarkResults.
tasks(data.table::data.table())
Table of included Tasks with three columns:
-
"task_hash" (character(1)),
-
"task_id" (character(1)), and
-
"task" (Task).
learners(data.table::data.table())
Table of included Learners with three columns:
-
"learner_hash" (character(1)),
-
"learner_id" (character(1)), and
-
"learner" (Learner).
Note that it is not feasible to access learned models via this field, as the training task would be ambiguous.
For this reason the returned learner are reset before they are returned.
Instead, select a row from the table returned by $score().
resamplings(data.table::data.table())
Table of included Resamplings with three columns:
-
"resampling_hash" (character(1)),
-
"resampling_id" (character(1)), and
-
"resampling" (Resampling).
resample_results(data.table::data.table())
Returns a table with three columns:
-
uhash (character()).
-
resample_result (ResampleResult).
n_resample_results(integer(1))
Returns the total number of stored ResampleResults.
uhashes(character())
Set of (unique) hashes of all included ResampleResults.
uhash_table(data.table::data.table)
Table with columns uhash, learner_id, task_id and resampling_id.
Methods
Public methods
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Method new()
Creates a new instance of this R6 class.
Usage
BenchmarkResult$new(data = NULL)
Arguments
data(ResultData)
An object of type ResultData, either extracted from another ResampleResult, another
BenchmarkResult, or manually constructed with as_result_data().
Method help()
Opens the help page for this object.
Usage
BenchmarkResult$help()
Method format()
Helper for print outputs.
Usage
BenchmarkResult$format(...)
Arguments
...(ignored).
Method print()
Printer.
Usage
BenchmarkResult$print()
Method combine()
Fuses a second BenchmarkResult into itself, mutating the BenchmarkResult in-place.
If the second BenchmarkResult bmr is NULL, simply returns self.
Note that you can alternatively use the combine function c() which calls this method internally.
Usage
BenchmarkResult$combine(bmr)
Arguments
bmr(BenchmarkResult)
A second BenchmarkResult object.
Returns
Returns the object itself, but modified by reference.
You need to explicitly $clone() the object beforehand if you want to keep
the object in its previous state.
Method marshal()
Marshals all stored models.
Usage
BenchmarkResult$marshal(...)
Arguments
...(any)
Additional arguments passed to marshal_model().
Method unmarshal()
Unmarshals all stored models.
Usage
BenchmarkResult$unmarshal(...)
Arguments
...(any)
Additional arguments passed to unmarshal_model().
Method score()
Returns a table with one row for each resampling iteration, including
all involved objects: Task, Learner, Resampling, iteration number
(integer(1)), and Prediction. If ids is set to TRUE, character
column of extracted ids are added to the table for convenient
filtering: "task_id", "learner_id", and "resampling_id".
Additionally calculates the provided performance measures and binds the
performance scores as extra columns. These columns are named using the id of
the respective Measure.
Usage
BenchmarkResult$score(
measures = NULL,
ids = TRUE,
conditions = FALSE,
predictions = TRUE
)
Arguments
measures(Measure | list of Measure)
Measure(s) to calculate.
ids(logical(1))
Adds object ids ("task_id", "learner_id", "resampling_id") as
extra character columns to the returned table.
conditions(logical(1))
Adds condition messages ("warnings", "errors") as extra
list columns of character vectors to the returned table
predictions(logical(1))
Additionally return prediction objects, one column for each predict_set of all learners combined.
Columns are named "prediction_train", "prediction_test" and "prediction_internal_valid",
if present.
Returns
data.table::data.table().
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 row_ids, truth, response and
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 $obs_loss, but they require an
additional transformation after aggregation, in this example taking the square-root.
Usage
BenchmarkResult$obs_loss(measures = NULL, predict_sets = "test")
Arguments
measures(Measure | list of Measure)
Measure(s) to calculate.
predict_sets(character())
The predict sets.
Method aggregate()
Returns a result table where resampling iterations are combined into
ResampleResults. A column with the aggregated performance score is
added for each Measure, named with the id of the respective measure.
The method for aggregation is controlled by the Measure, e.g. micro
aggregation, macro aggregation or custom aggregation. Most measures
default to macro aggregation.
Note that the aggregated performances just give a quick impression which
approaches work well and which approaches are probably underperforming.
However, the aggregates do not account for variance and cannot replace
a statistical test.
See mlr3viz to get a better impression via boxplots or
mlr3benchmark for critical difference plots and
significance tests.
For convenience, different flags can be set to extract more
information from the returned ResampleResult.
Usage
BenchmarkResult$aggregate(
measures = NULL,
ids = TRUE,
uhashes = FALSE,
params = FALSE,
conditions = FALSE
)
Arguments
measures(Measure | list of Measure)
Measure(s) to calculate.
ids(logical(1))
Adds object ids ("task_id", "learner_id", "resampling_id") as
extra character columns for convenient subsetting.
uhashes(logical(1))
Adds the uhash values of the ResampleResult as extra character
column "uhash".
params(logical(1))
Adds the hyperparameter values as extra list column "params". You
can unnest them with mlr3misc::unnest().
conditions(logical(1))
Adds the number of resampling iterations with at least one warning as
extra integer column "warnings", and the number of resampling
iterations with errors as extra integer column "errors".
Returns
data.table::data.table().
Method filter()
Subsets the benchmark result.
You can either directly provide the row IDs or the uhashes of the resample results to keep,
or use the learner_ids, task_ids and resampling_ids arguments to filter for learner, task and resampling IDs.
The three options are mutually exclusive.
Usage
BenchmarkResult$filter(
i = NULL,
uhashes = NULL,
learner_ids = NULL,
task_ids = NULL,
resampling_ids = NULL
)
Arguments
i(integer() | NULL)
The iteration values to filter for.
uhashes(character() | NULL)
The uhashes of the resample results to filter for.
learner_ids(character() | NULL)
The learner IDs to filter for.
task_ids(character() | NULL)
The task IDs to filter for.
resampling_ids(character() | NULL)
The resampling IDs to filter for.
Returns
Returns the object itself, but modified by reference.
You need to explicitly $clone() the object beforehand if you want to keeps
the object in its previous state.
Examples
design = benchmark_grid(
tsks(c("iris", "sonar")),
lrns(c("classif.debug", "classif.featureless")),
rsmp("holdout")
)
bmr = benchmark(design)
bmr
bmr2 = bmr$clone(deep = TRUE)
bmr2$filter(learner_ids = "classif.featureless")
bmr2
Method resample_result()
Retrieve the i-th ResampleResult, by position, by unique hash uhash or by learner,
task and resampling IDs.
All three options are mutually exclusive.
Usage
BenchmarkResult$resample_result(
i = NULL,
uhash = NULL,
task_id = NULL,
learner_id = NULL,
resampling_id = NULL
)
Arguments
i(integer(1) | NULL)
The iteration value to filter for.
uhash(character(1) | NULL)
The unique identifier to filter for.
task_id(character(1) | NULL)
The task ID to filter for.
learner_id(character(1) | NULL)
The learner ID to filter for.
resampling_id(character(1) | NULL)
The resampling ID to filter for.
Returns
ResampleResult.
Examples
design = benchmark_grid(
tsk("iris"),
lrns(c("classif.debug", "classif.featureless")),
rsmp("holdout")
)
bmr = benchmark(design)
bmr$resample_result(learner_id = "classif.featureless")
bmr$resample_result(i = 1)
bmr$resample_result(uhash = uhashes(bmr, learner_id = "classif.debug"))
Method discard()
Shrinks the BenchmarkResult by discarding parts of the internally stored data.
Note that certain operations might stop work, e.g. extracting
importance values from learners or calculating measures requiring the task's data.
Usage
BenchmarkResult$discard(backends = FALSE, models = FALSE)
Arguments
backends(logical(1))
If TRUE, the DataBackend is removed from all stored Tasks.
models(logical(1))
If TRUE, the stored model is removed from all Learners.
Returns
Returns the object itself, but modified by reference.
You need to explicitly $clone() the object beforehand if you want to keeps
the object in its previous state.
Method set_threshold()
Sets the threshold for the response prediction of classification learners, given they have
output a probability prediction for a binary classification task.
The resample results for which to change the threshold can either be specified directly
via uhashes, by selecting the specific iterations (i) or by filtering according to
learner, task and resampling IDs.
If none of the three options is specified, the threshold is set for all resample results.
Usage
BenchmarkResult$set_threshold(
threshold,
i = NULL,
uhashes = NULL,
learner_ids = NULL,
task_ids = NULL,
resampling_ids = NULL,
ties_method = "random"
)
Arguments
threshold(numeric(1))
Threshold value.
i(integer() | NULL)
The iteration values to filter for.
uhashes(character() | NULL)
The unique identifiers of the ResampleResults for which the threshold should be set.
learner_ids(character() | NULL)
The learner IDs for which the threshold should be set.
task_ids(character() | NULL)
The task IDs for which the threshold should be set.
resampling_ids(character() | NULL)
The resampling IDs for which the threshold should be set.
ties_method(character(1))
Method to handle ties in probabilities when selecting a class label.
Must be one of "random", "first" or "last" (corresponding to the same options in max.col()).
-
"random": Randomly select one of the tied class labels (default).
-
"first": Select the first class label among tied values.
-
"last": Select the last class label among tied values.
Examples
design = benchmark_grid(
tsk("sonar"),
lrns(c("classif.debug", "classif.featureless"), predict_type = "prob"),
rsmp("holdout")
)
bmr = benchmark(design)
bmr$set_threshold(0.8, learner_ids = "classif.featureless")
bmr$set_threshold(0.3, i = 2)
bmr$set_threshold(0.7, uhashes = uhashes(bmr, learner_ids = "classif.featureless"))
Method clone()
The objects of this class are cloneable with this method.
Usage
BenchmarkResult$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Note
All stored objects are accessed by reference.
Do not modify any extracted object without cloning it first.
See Also
Chapter in the mlr3book:
https://mlr3book.mlr-org.com/chapters/chapter3/evaluation_and_benchmarking.html#sec-benchmarking
Package mlr3viz for some generic visualizations.
-
mlr3benchmark for post-hoc analysis of benchmark results.
Other benchmark:
benchmark(),
benchmark_grid()
Examples
set.seed(123)
learners = list(
lrn("classif.featureless", predict_type = "prob"),
lrn("classif.rpart", predict_type = "prob")
)
design = benchmark_grid(
tasks = list(tsk("sonar"), tsk("penguins")),
learners = learners,
resamplings = rsmp("cv", folds = 3)
)
print(design)
bmr = benchmark(design)
print(bmr)
bmr$tasks
bmr$learners
# first 5 resampling iterations
head(as.data.table(bmr, measures = c("classif.acc", "classif.auc")), 5)
# aggregate results
bmr$aggregate()
# aggregate results with hyperparameters as separate columns
mlr3misc::unnest(bmr$aggregate(params = TRUE), "params")
# extract resample result for classif.rpart
rr = bmr$aggregate()[learner_id == "classif.rpart", resample_result][[1]]
print(rr)
# access the confusion matrix of the first resampling iteration
rr$predictions()[[1]]$confusion
# reduce to subset with task id "sonar"
bmr$filter(task_ids = "sonar")
print(bmr)
## ------------------------------------------------
## Method `BenchmarkResult$filter`
## ------------------------------------------------
design = benchmark_grid(
tsks(c("iris", "sonar")),
lrns(c("classif.debug", "classif.featureless")),
rsmp("holdout")
)
bmr = benchmark(design)
bmr
bmr2 = bmr$clone(deep = TRUE)
bmr2$filter(learner_ids = "classif.featureless")
bmr2
## ------------------------------------------------
## Method `BenchmarkResult$resample_result`
## ------------------------------------------------
design = benchmark_grid(
tsk("iris"),
lrns(c("classif.debug", "classif.featureless")),
rsmp("holdout")
)
bmr = benchmark(design)
bmr$resample_result(learner_id = "classif.featureless")
bmr$resample_result(i = 1)
bmr$resample_result(uhash = uhashes(bmr, learner_id = "classif.debug"))
## ------------------------------------------------
## Method `BenchmarkResult$set_threshold`
## ------------------------------------------------
design = benchmark_grid(
tsk("sonar"),
lrns(c("classif.debug", "classif.featureless"), predict_type = "prob"),
rsmp("holdout")
)
bmr = benchmark(design)
bmr$set_threshold(0.8, learner_ids = "classif.featureless")
bmr$set_threshold(0.3, i = 2)
bmr$set_threshold(0.7, uhashes = uhashes(bmr, learner_ids = "classif.featureless"))
mlr-org/mlr3 documentation built on July 4, 2025, 3:40 a.m.
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| BenchmarkResult | R Documentation |
Container for Benchmarking Results
Description
This is the result container object returned by benchmark().
A BenchmarkResult consists of the data of multiple ResampleResults.
The contents of a BenchmarkResult and ResampleResult are almost identical and the stored ResampleResults can be extracted via the $resample_result(i) method, where i is the index of the performed resample experiment.
This allows us to investigate the extracted ResampleResult and individual resampling iterations, as well as the predictions and models from each fold.
BenchmarkResults can be visualized via mlr3viz's autoplot() function.
For statistical analysis of benchmark results and more advanced plots, see mlr3benchmark.
S3 Methods
-
as.data.table(rr, ..., reassemble_learners = TRUE, convert_predictions = TRUE, predict_sets = "test", task_characteristics = FALSE)
BenchmarkResult ->data.table::data.table()
Returns a tabular view of the internal data. -
c(...)
(BenchmarkResult, ...) -> BenchmarkResult
Combines multiple objects convertible to BenchmarkResult into a new BenchmarkResult.
Active bindings
task_type(
character(1))
Task type of objects in theBenchmarkResult. All stored objects (Task, Learner, Prediction) in a singleBenchmarkResultare required to have the same task type, e.g.,"classif"or"regr". This isNAfor empty BenchmarkResults.tasks(
data.table::data.table())
Table of included Tasks with three columns:-
"task_hash"(character(1)), -
"task_id"(character(1)), and -
"task"(Task).
-
learners(
data.table::data.table())
Table of included Learners with three columns:-
"learner_hash"(character(1)), -
"learner_id"(character(1)), and -
"learner"(Learner).
Note that it is not feasible to access learned models via this field, as the training task would be ambiguous. For this reason the returned learner are reset before they are returned. Instead, select a row from the table returned by
$score().-
resamplings(
data.table::data.table())
Table of included Resamplings with three columns:-
"resampling_hash"(character(1)), -
"resampling_id"(character(1)), and -
"resampling"(Resampling).
-
resample_results(
data.table::data.table())
Returns a table with three columns:-
uhash(character()). -
resample_result(ResampleResult).
-
n_resample_results(
integer(1))
Returns the total number of stored ResampleResults.uhashes(
character())
Set of (unique) hashes of all included ResampleResults.uhash_table(data.table::data.table)
Table with columnsuhash,learner_id,task_idandresampling_id.
Methods
Public methods
Method new()
Creates a new instance of this R6 class.
Usage
BenchmarkResult$new(data = NULL)
Arguments
data(
ResultData)
An object of typeResultData, either extracted from another ResampleResult, another BenchmarkResult, or manually constructed withas_result_data().
Method help()
Opens the help page for this object.
Usage
BenchmarkResult$help()
Method format()
Helper for print outputs.
Usage
BenchmarkResult$format(...)
Arguments
...(ignored).
Method print()
Printer.
Usage
BenchmarkResult$print()
Method combine()
Fuses a second BenchmarkResult into itself, mutating the BenchmarkResult in-place.
If the second BenchmarkResult bmr is NULL, simply returns self.
Note that you can alternatively use the combine function c() which calls this method internally.
Usage
BenchmarkResult$combine(bmr)
Arguments
bmr(BenchmarkResult)
A second BenchmarkResult object.
Returns
Returns the object itself, but modified by reference.
You need to explicitly $clone() the object beforehand if you want to keep
the object in its previous state.
Method marshal()
Marshals all stored models.
Usage
BenchmarkResult$marshal(...)
Arguments
...(any)
Additional arguments passed tomarshal_model().
Method unmarshal()
Unmarshals all stored models.
Usage
BenchmarkResult$unmarshal(...)
Arguments
...(any)
Additional arguments passed tounmarshal_model().
Method score()
Returns a table with one row for each resampling iteration, including
all involved objects: Task, Learner, Resampling, iteration number
(integer(1)), and Prediction. If ids is set to TRUE, character
column of extracted ids are added to the table for convenient
filtering: "task_id", "learner_id", and "resampling_id".
Additionally calculates the provided performance measures and binds the performance scores as extra columns. These columns are named using the id of the respective Measure.
Usage
BenchmarkResult$score( measures = NULL, ids = TRUE, conditions = FALSE, predictions = TRUE )
Arguments
measures(Measure | list of Measure)
Measure(s) to calculate.ids(
logical(1))
Adds object ids ("task_id","learner_id","resampling_id") as extra character columns to the returned table.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 all learners combined. Columns are named"prediction_train","prediction_test"and"prediction_internal_valid", if present.
Returns
data.table::data.table().
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 row_ids, truth, response and
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 $obs_loss, but they require an
additional transformation after aggregation, in this example taking the square-root.
Usage
BenchmarkResult$obs_loss(measures = NULL, predict_sets = "test")
Arguments
measures(Measure | list of Measure)
Measure(s) to calculate.predict_sets(
character())
The predict sets.
Method aggregate()
Returns a result table where resampling iterations are combined into ResampleResults. A column with the aggregated performance score is added for each Measure, named with the id of the respective measure.
The method for aggregation is controlled by the Measure, e.g. micro aggregation, macro aggregation or custom aggregation. Most measures default to macro aggregation.
Note that the aggregated performances just give a quick impression which approaches work well and which approaches are probably underperforming. However, the aggregates do not account for variance and cannot replace a statistical test. See mlr3viz to get a better impression via boxplots or mlr3benchmark for critical difference plots and significance tests.
For convenience, different flags can be set to extract more information from the returned ResampleResult.
Usage
BenchmarkResult$aggregate( measures = NULL, ids = TRUE, uhashes = FALSE, params = FALSE, conditions = FALSE )
Arguments
measures(Measure | list of Measure)
Measure(s) to calculate.ids(
logical(1))
Adds object ids ("task_id","learner_id","resampling_id") as extra character columns for convenient subsetting.uhashes(
logical(1))
Adds the uhash values of the ResampleResult as extra character column"uhash".params(
logical(1))
Adds the hyperparameter values as extra list column"params". You can unnest them withmlr3misc::unnest().conditions(
logical(1))
Adds the number of resampling iterations with at least one warning as extra integer column"warnings", and the number of resampling iterations with errors as extra integer column"errors".
Returns
data.table::data.table().
Method filter()
Subsets the benchmark result.
You can either directly provide the row IDs or the uhashes of the resample results to keep,
or use the learner_ids, task_ids and resampling_ids arguments to filter for learner, task and resampling IDs.
The three options are mutually exclusive.
Usage
BenchmarkResult$filter( i = NULL, uhashes = NULL, learner_ids = NULL, task_ids = NULL, resampling_ids = NULL )
Arguments
i(
integer()|NULL)
The iteration values to filter for.uhashes(
character()|NULL)
The uhashes of the resample results to filter for.learner_ids(
character()|NULL)
The learner IDs to filter for.task_ids(
character()|NULL)
The task IDs to filter for.resampling_ids(
character()|NULL)
The resampling IDs to filter for.
Returns
Returns the object itself, but modified by reference.
You need to explicitly $clone() the object beforehand if you want to keeps
the object in its previous state.
Examples
design = benchmark_grid(
tsks(c("iris", "sonar")),
lrns(c("classif.debug", "classif.featureless")),
rsmp("holdout")
)
bmr = benchmark(design)
bmr
bmr2 = bmr$clone(deep = TRUE)
bmr2$filter(learner_ids = "classif.featureless")
bmr2
Method resample_result()
Retrieve the i-th ResampleResult, by position, by unique hash uhash or by learner,
task and resampling IDs.
All three options are mutually exclusive.
Usage
BenchmarkResult$resample_result( i = NULL, uhash = NULL, task_id = NULL, learner_id = NULL, resampling_id = NULL )
Arguments
i(
integer(1)|NULL)
The iteration value to filter for.uhash(
character(1)|NULL)
The unique identifier to filter for.task_id(
character(1)|NULL)
The task ID to filter for.learner_id(
character(1)|NULL)
The learner ID to filter for.resampling_id(
character(1)|NULL)
The resampling ID to filter for.
Returns
ResampleResult.
Examples
design = benchmark_grid(
tsk("iris"),
lrns(c("classif.debug", "classif.featureless")),
rsmp("holdout")
)
bmr = benchmark(design)
bmr$resample_result(learner_id = "classif.featureless")
bmr$resample_result(i = 1)
bmr$resample_result(uhash = uhashes(bmr, learner_id = "classif.debug"))
Method discard()
Shrinks the BenchmarkResult by discarding parts of the internally stored data. Note that certain operations might stop work, e.g. extracting importance values from learners or calculating measures requiring the task's data.
Usage
BenchmarkResult$discard(backends = FALSE, models = FALSE)
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.
Returns
Returns the object itself, but modified by reference.
You need to explicitly $clone() the object beforehand if you want to keeps
the object in its previous state.
Method set_threshold()
Sets the threshold for the response prediction of classification learners, given they have output a probability prediction for a binary classification task.
The resample results for which to change the threshold can either be specified directly
via uhashes, by selecting the specific iterations (i) or by filtering according to
learner, task and resampling IDs.
If none of the three options is specified, the threshold is set for all resample results.
Usage
BenchmarkResult$set_threshold( threshold, i = NULL, uhashes = NULL, learner_ids = NULL, task_ids = NULL, resampling_ids = NULL, ties_method = "random" )
Arguments
threshold(
numeric(1))
Threshold value.i(
integer()|NULL)
The iteration values to filter for.uhashes(
character()|NULL)
The unique identifiers of the ResampleResults for which the threshold should be set.learner_ids(
character()|NULL)
The learner IDs for which the threshold should be set.task_ids(
character()|NULL)
The task IDs for which the threshold should be set.resampling_ids(
character()|NULL)
The resampling IDs for which the threshold should be set.ties_method(
character(1))
Method to handle ties in probabilities when selecting a class label. Must be one of"random","first"or"last"(corresponding to the same options inmax.col()).-
"random": Randomly select one of the tied class labels (default). -
"first": Select the first class label among tied values. -
"last": Select the last class label among tied values.
-
Examples
design = benchmark_grid(
tsk("sonar"),
lrns(c("classif.debug", "classif.featureless"), predict_type = "prob"),
rsmp("holdout")
)
bmr = benchmark(design)
bmr$set_threshold(0.8, learner_ids = "classif.featureless")
bmr$set_threshold(0.3, i = 2)
bmr$set_threshold(0.7, uhashes = uhashes(bmr, learner_ids = "classif.featureless"))
Method clone()
The objects of this class are cloneable with this method.
Usage
BenchmarkResult$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Note
All stored objects are accessed by reference. Do not modify any extracted object without cloning it first.
See Also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter3/evaluation_and_benchmarking.html#sec-benchmarking
Package mlr3viz for some generic visualizations.
-
mlr3benchmark for post-hoc analysis of benchmark results.
Other benchmark:
benchmark(),
benchmark_grid()
Examples
set.seed(123)
learners = list(
lrn("classif.featureless", predict_type = "prob"),
lrn("classif.rpart", predict_type = "prob")
)
design = benchmark_grid(
tasks = list(tsk("sonar"), tsk("penguins")),
learners = learners,
resamplings = rsmp("cv", folds = 3)
)
print(design)
bmr = benchmark(design)
print(bmr)
bmr$tasks
bmr$learners
# first 5 resampling iterations
head(as.data.table(bmr, measures = c("classif.acc", "classif.auc")), 5)
# aggregate results
bmr$aggregate()
# aggregate results with hyperparameters as separate columns
mlr3misc::unnest(bmr$aggregate(params = TRUE), "params")
# extract resample result for classif.rpart
rr = bmr$aggregate()[learner_id == "classif.rpart", resample_result][[1]]
print(rr)
# access the confusion matrix of the first resampling iteration
rr$predictions()[[1]]$confusion
# reduce to subset with task id "sonar"
bmr$filter(task_ids = "sonar")
print(bmr)
## ------------------------------------------------
## Method `BenchmarkResult$filter`
## ------------------------------------------------
design = benchmark_grid(
tsks(c("iris", "sonar")),
lrns(c("classif.debug", "classif.featureless")),
rsmp("holdout")
)
bmr = benchmark(design)
bmr
bmr2 = bmr$clone(deep = TRUE)
bmr2$filter(learner_ids = "classif.featureless")
bmr2
## ------------------------------------------------
## Method `BenchmarkResult$resample_result`
## ------------------------------------------------
design = benchmark_grid(
tsk("iris"),
lrns(c("classif.debug", "classif.featureless")),
rsmp("holdout")
)
bmr = benchmark(design)
bmr$resample_result(learner_id = "classif.featureless")
bmr$resample_result(i = 1)
bmr$resample_result(uhash = uhashes(bmr, learner_id = "classif.debug"))
## ------------------------------------------------
## Method `BenchmarkResult$set_threshold`
## ------------------------------------------------
design = benchmark_grid(
tsk("sonar"),
lrns(c("classif.debug", "classif.featureless"), predict_type = "prob"),
rsmp("holdout")
)
bmr = benchmark(design)
bmr$set_threshold(0.8, learner_ids = "classif.featureless")
bmr$set_threshold(0.3, i = 2)
bmr$set_threshold(0.7, uhashes = uhashes(bmr, learner_ids = "classif.featureless"))
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