Measures the elapsed time during train ("time_train"), predict ("time_predict"), or both ("time_both").
Dictionary
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr()
:
Meta Information
Task type: “NA”
Range: \([0, \infty)\)
Minimize: TRUE
Average: macro
Required Prediction: “NA”
Required Packages: mlr3
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-eval
Package mlr3measures for the scoring functions. Dictionary of Measures: mlr_measures
as.data.table(mlr_measures)
for a table of available Measures in the running session (depending on the loaded packages).Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
Other Measure:
Measure
,
MeasureClassif
,
MeasureRegr
,
MeasureSimilarity
,
mlr_measures
,
mlr_measures_aic
,
mlr_measures_bic
,
mlr_measures_classif.costs
,
mlr_measures_debug_classif
,
mlr_measures_internal_valid_score
,
mlr_measures_oob_error
,
mlr_measures_selected_features
Super class
mlr3::Measure
-> MeasureElapsedTime
Public fields
stages
(
character()
)
Which stages of the learner to measure? Usually set during construction.
Methods
Method new()
Creates a new instance of this R6 class.
Usage
MeasureElapsedTime$new(id = "elapsed_time", stages)
Arguments
id
(
character(1)
)
Identifier for the new instance.stages
(
character()
)
Subset of("train", "predict")
. The runtime of provided stages will be summed.