Measures the elapsed time during train ("time_train"), predict ("time_predict"), or both ("time_both").
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
mlr_measures$get("time_train")
msr("time_train")
Task type: “NA”
Range: \([0, \infty)\)
Minimize: TRUE
Average: macro
Required Prediction: “NA”
Required Packages: mlr3
Empty ParamSet
mlr3::Measure -> MeasureElapsedTime
stages(character())
Which stages of the learner to measure?
Usually set during construction.
new()Creates a new instance of this R6 class.
MeasureElapsedTime$new(id = "elapsed_time", stages)id(character(1))
Identifier for the new instance.
stages(character())
Subset of ("train", "predict").
The runtime of provided stages will be summed.
clone()The objects of this class are cloneable with this method.
MeasureElapsedTime$clone(deep = FALSE)deepWhether to make a deep clone.
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_oob_error,
mlr_measures_selected_features