This measure specializes Measure for classification problems:
task_type is set to "classif".
Possible values for predict_type are "response" and "prob".
Predefined measures can be found in the dictionary mlr_measures.
The default measure for classification is classif.ce.
mlr3::Measure -> MeasureClassif
new()Creates a new instance of this R6 class.
MeasureClassif$new(
id,
param_set = ps(),
range,
minimize = NA,
average = "macro",
aggregator = NULL,
properties = character(),
predict_type = "response",
predict_sets = "test",
task_properties = character(),
packages = character(),
label = NA_character_,
man = NA_character_
)id(character(1))
Identifier for the new instance.
param_set(paradox::ParamSet)
Set of hyperparameters.
range(numeric(2))
Feasible range for this measure as c(lower_bound, upper_bound).
Both bounds may be infinite.
minimize(logical(1))
Set to TRUE if good predictions correspond to small values,
and to FALSE if good predictions correspond to large values.
If set to NA (default), tuning this measure is not possible.
average(character(1))
How to average multiple Predictions from a ResampleResult.
The default, "macro", calculates the individual performances scores for each Prediction and then uses the
function defined in $aggregator to average them to a single number.
If set to "micro", the individual Prediction objects are first combined into a single new Prediction object which is then used to assess the performance.
The function in $aggregator is not used in this case.
aggregator(function())
Function to aggregate over multiple iterations. The role of this function depends on
the value of field "average":
"macro": A numeric vector of scores (one per iteration) is passed.
The aggregate function defaults to mean() in this case.
"micro": The aggregator function is not used.
Instead, predictions from multiple iterations are first combined and then
scored in one go.
"custom": A ResampleResult is passed to the aggregate function.
properties(character())
Properties of the measure.
Must be a subset of mlr_reflections$measure_properties.
Supported by mlr3:
"requires_task" (requires the complete Task),
"requires_learner" (requires the trained Learner),
"requires_model" (requires the trained Learner, including the fitted
model),
"requires_train_set" (requires the training indices from the Resampling), and
"na_score" (the measure is expected to occasionally return NA or NaN).
"primary_iters" (the measure explictly handles resamplings that only use a subset
of their iterations for the point estimate).
"requires_no_prediction" (No prediction is required; This usually means that the
measure extracts some information from the learner state.).
predict_type(character(1))
Required predict type of the Learner.
Possible values are stored in mlr_reflections$learner_predict_types.
predict_sets(character())
Prediction sets to operate on, used in aggregate() to extract the matching predict_sets from the ResampleResult.
Multiple predict sets are calculated by the respective Learner during resample()/benchmark().
Must be a non-empty subset of {"train", "test", "internal_valid"}.
If multiple sets are provided, these are first combined to a single prediction object.
Default is "test".
task_properties(character())
Required task properties, see Task.
packages(character())
Set of required packages.
A warning is signaled by the constructor if at least one of the packages is not installed,
but loaded (not attached) later on-demand via requireNamespace().
label(character(1))
Label for the new instance.
man(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object.
The referenced help package can be opened via method $help().
clone()The objects of this class are cloneable with this method.
MeasureClassif$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,
MeasureRegr,
MeasureSimilarity,
mlr_measures,
mlr_measures_aic,
mlr_measures_bic,
mlr_measures_classif.costs,
mlr_measures_debug_classif,
mlr_measures_elapsed_time,
mlr_measures_internal_valid_score,
mlr_measures_oob_error,
mlr_measures_regr.rsq,
mlr_measures_selected_features