This measure specializes Measure for measures quantifying the similarity of sets of selected features. To calculate similarity measures, the Learner must have the property "selected_features".
task_type is set to NA_character_.
average is set to "custom".
Predefined measures can be found in the dictionary
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 Prediction s 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()`.
Method clone()
The objects of this class are cloneable with this method.