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R/LearnerAvg.R changed.
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R/PipeOpSmote.R changed.
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R/PipeOpImputeOOR.R changed.
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#' for `LearnerClassifAvg` and `regr.mse` for `LearnerRegrAvg`).
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#' Learned weights can be obtained from `$model`.
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#' Using non-linear optimization is implemented in the SuperLearner R package.
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#' For a more detailed analysis the reader is referred to
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#' *LeDell, 2015: Scalable Ensemble Learning and Computationally Efficient Variance Estimation*.
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#' For a more detailed analysis the reader is referred to LeDell (2015).
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#'
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#' @section Parameter Set:
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#' * `measure` :: `character(1)` | `Measure` \cr
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#' * `LearnerRegrAvg$new(), id = "regr.avg")` \cr
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#'   (`chr`) -> `self` \cr
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#'   Constructor.
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#'
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#' @references
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#' \cite{mlr3pipelines}{ledell_2015}
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#'
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#' @family Learners
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#' @family Ensembles
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#' @include PipeOpEnsemble.R

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#'   Desired times of synthetic minority instances over the original number of
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#'   majority instances. See [`SMOTE()`][`smotefamily::SMOTE`].
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#'
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#' @section Internals:
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#' For details see: \cr
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#' Chawla, N., Bowyer, K., Hall, L. and Kegelmeyer, W. 2002.\cr
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#' SMOTE: Synthetic minority oversampling technique.\cr
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#' Journal of Artificial Intelligence Research. 16, 321-357.
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#'
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#' @section Fields:
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#' Only fields inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
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#'
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#' @section Methods:
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#' Only methods inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
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#'
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#' @references
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#' \cite{mlr3pipelines}{chawla_2002}
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#'
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#' @family PipeOps
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#' @include PipeOpTaskPreproc.R
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#' @export

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#' @section Methods:
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#' Only methods inherited from [`PipeOpImpute`]/[`PipeOp`].
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#'
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#' @references
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#' \cite{mlr3pipelines}{ding_2010}
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#'
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#' @examples
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#' library("mlr3")
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#' set.seed(2409)
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