Classification Measure

This measure specializes Measure for classification problems. Predefined measures can be found in the Dictionary mlr_measures.

The task_type is set to "classif".

Format

R6::R6Class object inheriting from Measure.

m = MeasureClassif$new(id, range, minimize, predict_type = "response",
     task_properties = character(0L), packages = character(0L))

id :: character(1)
Identifier for the measure.
range :: numeric(2)
Feasible range for this measure as c(lower_bound, upper_bound).
minimize :: logical(1)
Set to TRUE if good predictions correspond to small values.
predict_type :: character(1)
Required predict type of the Learner.
task_properties :: character()
Required task properties, see Task.
packages :: character()
Set of required packages. Note that these packages will be loaded via requireNamespace(), and are not attached.

id :: character(1)
Stores the identifier of the measure.
minimize :: logical(1)
Is TRUE if the best value is reached via minimization and FALSE by maximization.
packages :: character()
Stores the names of required packages.
range :: numeric(2)
Stores the feasible range of the measure.
task_type :: character(1)
Stores the required type of the Task.
task_properties :: character()
Stores required properties of the Task.

aggregate(rr)
ResampleResult -> numeric(1)
Aggregates multiple performance scores into a single score using the aggregate function of the measure. Operates on a ResampleResult as returned by resample.
calculate(e)
Experiment -> numeric(1)
Takes an Experiment, extracts the predictions (as well as other possibly needed objects), and calculates a score.