R/MeasureClassif.R
This measure specializes Measure for classification problems. Predefined measures can be found in the Dictionary mlr_measures.
The task_type
is set to "classif"
.
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.
Other Measure: MeasureRegr
,
Measure
, mlr_measures