Measures the elapsed time during train ("time_train"), predict ("time_predict"), or both ("time_both").
Aggregation of elapsed time defaults to mean but can be configured via the field aggregator of the Measure.
When predictions for multiple predict sets were made during resample() or benchmark(), the predict time shows the cumulative duration of all predictions.
If learner$predict() is called manually, the last predict time gets overwritten.
The elapsed time accounts only for the training duration of the primary learner, excluding the time required for training the fallback learner.
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_regr.rsq,
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.