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.