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Optimized Weighted Average of Features for Classification and Regression
Source: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 $model
.
This Learner implements and generalizes an approach proposed in LeDell (2015) that uses non-linear
optimization in order to learn base-learner weights that optimize a given performance metric (e.g AUC
).
The approach is similar but not exactly the same as the one implemented as AUC
in the SuperLearner
R package (when metric
is "classif.auc"
).
For a more detailed analysis and the general idea, the reader is referred to LeDell (2015).
Note, that weights always sum to 1 by division by sum(weights)
before weighting
incoming features.
Format
R6Class
object inheriting from mlr3::LearnerClassif
/mlr3::Learner
.
Parameters
The parameters are the parameters inherited from LearnerClassif
, as well as:
measure
::Measure
|character
Measure
to optimize for. Will be converted to aMeasure
in case it ischaracter
. Initialized to"classif.ce"
, i.e. misclassification error for classification and"regr.mse"
, i.e. mean squared error for regression.optimizer
::Optimizer
|character(1)
Optimizer
used to find optimal thresholds. Ifcharacter
, converts toOptimizer
viaopt
. Initialized toOptimizerNLoptr
. Nloptr hyperparameters are initialized toxtol_rel = 1e-8
,algorithm = "NLOPT_LN_COBYLA"
and equal initial weights for each learner. For more fine-grained control, it is recommended to supply a instantiatedOptimizer
.log_level
::character(1)
|integer(1)
Set a temporary log-level forlgr::get_logger("bbotk")
. Initialized to: "warn".
Methods
LearnerClassifAvg$new(), id = "classif.avg")
(chr
) ->self
Constructor.LearnerRegrAvg$new(), id = "regr.avg")
(chr
) ->self
Constructor.
References
LeDell, Erin (2015). Scalable Ensemble Learning and Computationally Efficient Variance Estimation. Ph.D. thesis, UC Berkeley.
See also
Other Learners:
mlr_learners_graph
Other Ensembles:
PipeOpEnsemble
,
mlr_pipeops_classifavg
,
mlr_pipeops_ovrunite
,
mlr_pipeops_regravg