makeAggregation: Specify your own aggregation of measures.

Description Usage Arguments Value See Also Examples

View source: R/Aggregation.R

Description

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

Usage

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

Arguments

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.

Value

[Aggregation].

See Also

aggregations, setAggregation

Examples

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# 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))

berndbischl/mlr documentation built on Nov. 21, 2017, 12:51 a.m.