Construct your own performance measure, used after resampling.
Note that individual training / test set performance values will be set to `NA`, you
only calculate an aggregated value. If you can define a function that makes sense
for every single training / test set, implement your own [Measure].
makeCustomResampledMeasure(measure.id, aggregation.id, minimize = TRUE,
properties = character(0L), fun, extra.args = list(), best = NULL,
worst = NULL, measure.name = measure.id,
aggregation.name = aggregation.id, note = "")
Arguments
| measure.id |
(`character(1)`)
Short name of measure. |
| aggregation.id |
(`character(1)`)
Short name of aggregation. |
| minimize |
(`logical(1)`)
Should the measure be minimized?
Default is `TRUE`. |
| properties |
([character])
Set of measure properties. For a list of values see [Measure].
Default is `character(0)`. |
| fun |
(`function(task, group, pred, extra.args)`)
Calculates performance value from [ResamplePrediction] object.
For rare cases you can also use the task, the grouping or the extra arguments `extra.args`.
- `task` ([Task])
The task. - `group` ([factor])
Grouping of resampling iterations. This encodes whether specific iterations
'belong together' (e.g. repeated CV). - `pred` ([Prediction])
Prediction object. - `extra.args` ([list])
See below. |
| extra.args |
([list])
List of extra arguments which will always be passed to `fun`.
Default is empty list. |
| best |
(`numeric(1)`)
Best obtainable value for measure.
Default is -`Inf` or `Inf`, depending on `minimize`. |
| worst |
(`numeric(1)`)
Worst obtainable value for measure.
Default is `Inf` or -`Inf`, depending on `minimize`. |
| measure.name |
(`character(1)`)
Long name of measure.
Default is `measure.id`. |
| aggregation.name |
(`character(1)`)
Long name of the aggregation.
Default is `aggregation.id`. |
| note |
([character])
Description and additional notes for the measure. Default is “”. |
Value
Measure.
See also