Commit ⋅ mlr-org/mlr3pipelines

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man/mlr_pipeops_smote.Rd has changed.

@@ -14,8 +14,7 @@

#' for `LearnerClassifAvg` and `regr.mse` for `LearnerRegrAvg`).
#' Learned weights can be obtained from `$model`.
#' Using non-linear optimization is implemented in the SuperLearner R package.
#' For a more detailed analysis the reader is referred to
#' *LeDell, 2015: Scalable Ensemble Learning and Computationally Efficient Variance Estimation*.
#' For a more detailed analysis the reader is referred to LeDell (2015).
#'
#' @section Parameter Set:
#' * `measure` :: `character(1)` | `Measure` \cr

@@ -35,6 +34,10 @@

#' * `LearnerRegrAvg$new(), id = "regr.avg")` \cr
#'   (`chr`) -> `self` \cr
#'   Constructor.
#'
#' @references
#' \cite{mlr3pipelines}{ledell_2015}
#'
#' @family Learners
#' @family Ensembles
#' @include PipeOpEnsemble.R

@@ -40,18 +40,15 @@

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

@@ -56,6 +56,9 @@

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

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Project Totals (77 files)	2,528	2,388	0	140	94.46%

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14	14		#' for `LearnerClassifAvg` and `regr.mse` for `LearnerRegrAvg`).
15	15		#' Learned weights can be obtained from `$model`.
16	16		#' Using non-linear optimization is implemented in the SuperLearner R package.
17		-	#' For a more detailed analysis the reader is referred to
18		-	#' LeDell, 2015: Scalable Ensemble Learning and Computationally Efficient Variance Estimation.
	17	+	#' For a more detailed analysis the reader is referred to LeDell (2015).
19	18		#'
20	19		#' @section Parameter Set:
21	20		#' * `measure` :: `character(1)` \| `Measure` \cr

35	34		#' * `LearnerRegrAvg$new(), id = "regr.avg")` \cr
36	35		#' (`chr`) -> `self` \cr
37	36		#' Constructor.
	37	+	#'
	38	+	#' @references
	39	+	#' \cite{mlr3pipelines}{ledell_2015}
	40	+	#'
38	41		#' @family Learners
39	42		#' @family Ensembles
40	43		#' @include PipeOpEnsemble.R

40	40		#' Desired times of synthetic minority instances over the original number of
41	41		#' majority instances. See [`SMOTE()`][`smotefamily::SMOTE`].
42	42		#'
43		-	#' @section Internals:
44		-	#' For details see: \cr
45		-	#' Chawla, N., Bowyer, K., Hall, L. and Kegelmeyer, W. 2002.\cr
46		-	#' SMOTE: Synthetic minority oversampling technique.\cr
47		-	#' Journal of Artificial Intelligence Research. 16, 321-357.
48		-	#'
49	43		#' @section Fields:
50	44		#' Only fields inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
51	45		#'
52	46		#' @section Methods:
53	47		#' Only methods inherited from [`PipeOpTaskPreproc`]/[`PipeOp`].
54	48		#'
	49	+	#' @references
	50	+	#' \cite{mlr3pipelines}{chawla_2002}
	51	+	#'
55	52		#' @family PipeOps
56	53		#' @include PipeOpTaskPreproc.R
57	54		#' @export

56	56		#' @section Methods:
57	57		#' Only methods inherited from [`PipeOpImpute`]/[`PipeOp`].
58	58		#'
	59	+	#' @references
	60	+	#' \cite{mlr3pipelines}{ding_2010}
	61	+	#'
59	62		#' @examples
60	63		#' library("mlr3")
61	64		#' set.seed(2409)