# mlr_learners_avg

##### Optimized Weighted Average of Features for Classification and Regression

Computes a weighted average of inputs. Used in the context of computing weighted averages of predictions.

Predictions are averaged using `weights`

(in order of appearance in the data) which are optimized using
nonlinear optimization from the package "nloptr" for a measure provided in `measure`

(defaults to `classif.acc`

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*.

- Keywords
- datasets

##### Usage

`mlr_learners_classif.avg`mlr_learners_regr.avg

##### Format

`R6Class`

object inheriting from `mlr3::LearnerClassif`

/`mlr3::Learner`

.

##### Parameter Set

`measure`

::`character(1)`

|`Measure`

Measure to optimized weights for. The Measure is either obtained from`mlr_measures`

or directly supplied. Defaults to`classif.acc`

for`LearnerClassifAvg`

and`regr.mse`

for`LearnerRegrAvg`

`algorithm`

::`character(1)`

Several nonlinear optimization methods from`nloptr`

are available. See`nloptr::nloptr.print.options()`

for a list of possible options. Note that we only allow for derivative free local or global algorithms, i.e. NLOPT_(G|L)N_.

##### Methods

`LearnerClassifAvg$new(), id = "classif.avg")`

(`chr`

) ->`self`

Constructor.`LearnerRegrAvg$new(), id = "regr.avg")`

(`chr`

) ->`self`

Constructor.

##### See Also

Other Learners: `mlr_learners_graph`

Other Ensembles: `PipeOpEnsemble`

,
`mlr_pipeops_classifavg`

,
`mlr_pipeops_regravg`

*Documentation reproduced from package mlr3pipelines, version 0.1.1, License: LGPL-3*