This is the abstract base class for measures like MeasureClassif and MeasureRegr.

Measures are classes tailored around two functions doing the work:

A function

`$score()`

which quantifies the performance by comparing the truth and predictions.A function

`$aggregator()`

which combines multiple performance scores returned by`$score()`

to a single numeric value.

In addition to these two functions, meta-information about the performance measure is stored.

Predefined measures are stored in the dictionary mlr_measures,
e.g. `classif.auc`

or `time_train`

.
Many of the measures in mlr3 are implemented in mlr3measures as ordinary functions.

A guide on how to extend mlr3 with custom measures can be found in the mlr3book.

## Inheriting

For some measures (such as confidence intervals from `mlr3inferr`

) it is necessary that a measure
returns more than one value.
In such cases it is necessary to overwrite the public methods `$aggregate()`

and/or `$score()`

to return a named `numeric()`

where at least one of its names corresponds to the `id`

of the measure itself.

## See also

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:
`MeasureClassif`

,
`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`

## Public fields

`id`

(

`character(1)`

)

Identifier of the object. Used in tables, plot and text output.`label`

(

`character(1)`

)

Label for this object. Can be used in tables, plot and text output instead of the ID.`task_type`

(

`character(1)`

)

Task type, e.g.`"classif"`

or`"regr"`

.For a complete list of possible task types (depending on the loaded packages), see

`mlr_reflections$task_types$type`

.`param_set`

(paradox::ParamSet)

Set of hyperparameters.`obs_loss`

(

`function()`

|`NULL`

) Function to calculate the observation-wise loss.`trafo`

(

`list()`

|`NULL`

)`NULL`

or a list with two elements:`trafo`

: the transformation function applied after aggregating observation-wise losses (e.g.`sqrt`

for RMSE)`deriv`

: The derivative of the`trafo`

.

`predict_type`

(

`character(1)`

)

Required predict type of the Learner.`check_prerequisites`

(

`character(1)`

)

How to proceed if one of the following prerequisites is not met:wrong predict type (e.g., probabilities required, but only labels available).

wrong predict set (e.g., learner predicted on training set, but predictions of test set required).

task properties not satisfied (e.g., binary classification measure on multiclass task).

Possible values are

`"ignore"`

(just return`NaN`

) and`"warn"`

(default, raise a warning before returning`NaN`

).`task_properties`

(

`character()`

)

Required properties of the Task.`range`

(

`numeric(2)`

)

Lower and upper bound of possible performance scores.`properties`

(

`character()`

)

Properties of this measure.`minimize`

(

`logical(1)`

)

If`TRUE`

, good predictions correspond to small values of performance scores.`packages`

(

`character(1)`

)

Set of required packages. These packages are loaded, but not attached.`man`

(

`character(1)`

)

String in the format`[pkg]::[topic]`

pointing to a manual page for this object. Defaults to`NA`

, but can be set by child classes.

## Active bindings

`predict_sets`

(

`character()`

)

During`resample()`

/`benchmark()`

, a Learner can predict on multiple sets. Per default, a learner only predicts observations in the test set (`predict_sets == "test"`

). To change this behavior, set`predict_sets`

to a non-empty subset of`{"train", "test", "internal_valid"}`

. The`"train"`

predict set contains the train ids from the resampling. This means that if a learner does validation and sets`$validate`

to a ratio (creating the validation data from the training data), the train predictions will include the predictions for the validation data. Each set yields a separate Prediction object. Those can be combined via getters in ResampleResult/BenchmarkResult, or Measures can be configured to operate on specific subsets of the calculated prediction sets.`hash`

(

`character(1)`

)

Hash (unique identifier) for this object.`average`

(

`character(1)`

)

Method for aggregation:`"micro"`

: All predictions from multiple resampling iterations are first combined into a single Prediction object. Next, the scoring function of the measure is applied on this combined object, yielding a single numeric score.`"macro"`

: The scoring function is applied on the Prediction object of each resampling iterations, each yielding a single numeric score. Next, the scores are combined with the`aggregator`

function to a single numerical score.`"custom"`

: The measure comes with a custom aggregation method which directly operates on a ResampleResult.

`aggregator`

(

`function()`

)

Function to aggregate scores computed on different resampling iterations.

## Methods

### Method `new()`

Creates a new instance of this R6 class.

Note that this object is typically constructed via a derived classes, e.g. MeasureClassif or MeasureRegr.

#### Usage

```
Measure$new(
id,
task_type = NA,
param_set = ps(),
range = c(-Inf, Inf),
minimize = NA,
average = "macro",
aggregator = NULL,
obs_loss = NULL,
properties = character(),
predict_type = "response",
predict_sets = "test",
task_properties = character(),
packages = character(),
label = NA_character_,
man = NA_character_,
trafo = NULL
)
```

#### Arguments

`id`

(

`character(1)`

)

Identifier for the new instance.`task_type`

(

`character(1)`

)

Type of task, e.g.`"regr"`

or`"classif"`

. Must be an element of mlr_reflections$task_types$type.`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.

`obs_loss`

(

`function`

or`NULL`

)

The observation-wise loss function, e.g. zero-one for classification error.`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()`

.`trafo`

(

`list()`

or`NULL`

)

An optional list with two elements, containing the transformation`"fn"`

and its derivative`"deriv"`

. The transformation function is the function that is applied after aggregating the pointwise losses, i.e. this requires an`$obs_loss`

to be present. An example is`sqrt`

for RMSE.

### Method `score()`

Takes a Prediction (or a list of Prediction objects named with valid `predict_sets`

)
and calculates a numeric score.
If the measure if flagged with the properties `"requires_task"`

, `"requires_learner"`

,
`"requires_model"`

or `"requires_train_set"`

, you must additionally
pass the respective Task, the (trained) Learner or the training set indices.
This is handled internally during `resample()`

/`benchmark()`

.

#### Arguments

`prediction`

(Prediction | named list of Prediction).

`task`

(Task).

`learner`

(Learner).

`train_set`

(

`integer()`

).

### Method `aggregate()`

Aggregates multiple performance scores into a single score, e.g. by using the `aggregator`

function of the measure.