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

makeAggregation

Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There is
    also an experimental extension for survival analysis, clustering and
    general, example-specific cost-sensitive learning. Generic resampling,
    including cross-validation, bootstrapping and subsampling. Hyperparameter
    tuning with modern optimization techniques, for single- and multi-objective
    problems. Filter and wrapper methods for feature selection. Extension of
    basic learners with additional operations common in machine learning, also
    allowing for easy nested resampling. Most operations can be parallelized.

makeAggregation: Specifiy your own aggregation of measures

Description

Usage

Arguments

Value

See Also

Examples