A simple Dictionary storing objects of class Measure. Each measure has an associated help page, see mlr_measures_[id].

Format

R6::R6Class object.

Methods

  • get(key, ...)
    (character(1), ...) -> any
    Retrieves object with key key from the dictionary.

  • mget(keys, ...)
    (character(), ...) -> named list()
    Retrieves objects with keys keys from the dictionary, returns them in a list named with keys.

  • has(keys)
    character() -> logical()
    Returns a logical vector with TRUE at its i-th position, if the i-th key exists.

  • keys(pattern)
    character(1) -> character()
    Returns all keys which comply to the regular expression pattern.

  • add(key, value)
    (character(1), any) -> self
    Adds object value to the dictionary with key key, potentially overwriting a previously stored value.

  • remove(key)
    character() -> self
    Removes object with key key from the dictionary.

S3 methods

See also

Examples

as.data.table(mlr_measures)
#> key task_type predict_type packages #> 1: classif.acc classif response Metrics #> 2: classif.auc classif prob Metrics #> 3: classif.costs classif response #> 4: classif.f1 classif prob Metrics #> 5: classif.fdr classif response #> 6: classif.fn classif response #> 7: classif.fnr classif response #> 8: classif.for classif response #> 9: classif.fp classif response #> 10: classif.fpr classif response #> 11: classif.mmce classif response Metrics #> 12: classif.npv classif response #> 13: classif.ppv classif response #> 14: classif.precision classif response #> 15: classif.recall classif response #> 16: classif.sensitivity classif response #> 17: classif.specificity classif response #> 18: classif.tn classif response #> 19: classif.tnr classif response #> 20: classif.tp classif response #> 21: classif.tpr classif response #> 22: oob_error <NA> response #> 23: regr.mae regr response Metrics #> 24: regr.mse regr response Metrics #> 25: selected_features <NA> response #> 26: time_both <NA> response #> 27: time_predict <NA> response #> 28: time_train <NA> response #> key task_type predict_type packages
mlr_measures$get("classif.mmce")
#> <MeasureClassifMMCE:classif.mmce> #> Packages: Metrics #> Range: [0, 1] #> Minimize: TRUE #> Predict type: response