`R/LearnerAvg.R`

`mlr_learners_avg.Rd`

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.ce`

for `LearnerClassifAvg`

and `regr.mse`

for `LearnerRegrAvg`

).
Learned weights can be obtained from `$model`

.
This Learner implements and generalizes an approach proposed in LeDell (2015) that uses non-linear
optimization in order to learn base-learner weights that optimize a given performance metric (e.g `AUC`

).
The approach is similar but not exactly the same as the one implemented as `AUC`

in the SuperLearner
R package (when `metric`

is `"classif.auc"`

).
For a more detailed analysis and the general idea, the reader is referred to LeDell (2015).

Note, that weights always sum to 1 by division by `sum(weights)`

before weighting
incoming features.

mlr_learners_classif.avg mlr_learners_regr.avg

`R6Class`

object inheriting from `mlr3::LearnerClassif`

/`mlr3::Learner`

.

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".

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

(`chr`

) ->`self`

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

(`chr`

) ->`self`

Constructor.

LeDell, Erin (2015).
*Scalable Ensemble Learning and Computationally Efficient Variance Estimation*.
Ph.D. thesis, UC Berkeley.

Other Learners:
`mlr_learners_graph`

Other Ensembles:
`PipeOpEnsemble`

,
`mlr_pipeops_classifavg`

,
`mlr_pipeops_ovrunite`

,
`mlr_pipeops_regravg`