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mlr (version 2.19.0)

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