mlr (version 2.17.1)

aggregations: 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.

Arguments

See Also

Aggregation