makeAggregation: Specify your own aggregation of measures.
In riebetob/mlr: Machine Learning in R

Description Usage Arguments Value See Also Examples

This is an advanced feature of mlr. It gives access to some inner workings so the result might not be compatible with everything!

1	makeAggregation(id, name = id, properties, fun)

`id`	[`character(1)`] Name of the aggregation method (preferably the same name as the generated function).
`name`	[`character(1)`] Long name of the aggregation method. Default is `id`.
`properties`	[`character`] Set of aggregation properties. req.train Are prediction or train sets required to calculate the aggregation? req.test Are prediction or test sets required to calculate the aggregation?
`fun`	[`function(task, perf.test, perf.train, measure, group, pred)`] Calculates the aggregated performance. In most cases you will only need the performances `perf.test` and optionally `perf.train` on the test and training data sets. `task` [`Task`] The task. `perf.test` [`numeric`] `performance` results on the test data sets. `perf.train` [`numeric`] `performance` results on the training data sets. `measure` [`Measure`] Performance measure. `group` [`factor`] Grouping of resampling iterations. This encodes whether specific iterations 'belong together' (e.g. repeated CV). `pred` [`Prediction`] Prediction object.

[Aggregation].

aggregations, setAggregation

# computes the interquartile range on all performance values
test.iqr = makeAggregation(id = "test.iqr", name = "Test set interquartile range",
  properties = "req.test",
  fun = function (task, perf.test, perf.train, measure, group, pred) IQR(perf.test))