# 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).

Note, that weights always sum to 1 by dividing through sum(weights) before weighting incoming features.

##### Usage

`mlr_learners_classif.avg`mlr_learners_regr.avg

##### Format

`R6Class`

object inheriting from `mlr3::LearnerClassif`

/`mlr3::Learner`

.

##### Parameters

The parameters are the parameters inherited from `LearnerClassif`

, as well as:

`measure`

::`Measure`

|`character`

`Measure`

to optimize for. Will be converted to a`Measure`

in case it is`character`

. Initialized to`"classif.ce"`

, i.e. misclassification error for classification and`"regr.mse"`

, i.e. mean squared error for regression.`optimizer`

::`Optimizer`

|`character(1)`

`Optimizer`

used to find optimal thresholds. If`character`

, converts to`Optimizer`

via`opt`

. Initialized to`OptimizerNLoptr`

. Nloptr hyperparameters are initialized to`xtol_rel = 1e-8`

,`algorithm = "NLOPT_LN_COBYLA"`

and equal initial weights for each learner. For more fine-grained control, it is recommended to supply a instantiated`Optimizer`

.`log_level`

::`character(1)`

|`integer(1)`

Set a temporary log-level for`lgr::get_logger("bbotk")`

. Initialized to: "warn".

##### Methods

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

(`chr`

) ->`self`

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

(`chr`

) ->`self`

Constructor.

##### References

mlr3pipelinesledell_2015

##### See Also

Other Learners:
`mlr_learners_graph`

Other Ensembles:
`PipeOpEnsemble`

,
`mlr_pipeops_classifavg`

,
`mlr_pipeops_ovrunite`

,
`mlr_pipeops_regravg`

*Documentation reproduced from package mlr3pipelines, version 0.3.0, License: LGPL-3*