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
Learned weights can be obtained from
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.
The parameters are the parameters inherited from
LearnerClassif, as well as:
Measure to optimize for.
Will be converted to a
Measure in case it is
"classif.ce", i.e. misclassification error for classification
"regr.mse", i.e. mean squared error for regression.
Optimizer used to find optimal thresholds.
character, converts to
opt. Initialized to
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
LearnerClassifAvg$new(), id = "classif.avg")
LearnerRegrAvg$new(), id = "regr.avg")
LeDell, Erin (2015). Scalable Ensemble Learning and Computationally Efficient Variance Estimation. Ph.D. thesis, UC Berkeley.