aggregations: Aggregation methods.

In mlr: Machine Learning in R

Aggregation methods.

Description

test.mean

Mean of performance values on test sets.

test.sd

Standard deviation of performance values on test sets.

test.median

Median of performance values on test sets.

test.min

Minimum of performance values on test sets.

test.max

Maximum of performance values on test sets.

test.sum

Sum of performance values on test sets.

train.mean

Mean of performance values on training sets.

train.sd

Standard deviation of performance values on training sets.

train.median

Median of performance values on training sets.

train.min

Minimum of performance values on training sets.

train.max

Maximum of performance values on training sets.

train.sum

Sum of performance values on training sets.

b632

Aggregation for B632 bootstrap.

b632plus

Aggregation for B632+ bootstrap.

testgroup.mean

Performance values on test sets are grouped according to resampling method. The mean for every group is calculated, then the mean of those means. Mainly used for repeated CV.

testgroup.sd

Similar to testgroup.mean - after the mean for every group is calculated, the standard deviation of those means is obtained. Mainly used for repeated CV.

test.join

Performance measure on joined test sets. This is especially useful for small sample sizes where unbalanced group sizes have a significant impact on the aggregation, especially for cross-validation test.join might make sense now. For the repeated CV, the performance is calculated on each repetition and then aggregated with the arithmetic mean.

See Also

Aggregation

mlr documentation built on June 22, 2024, 10:51 a.m.