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
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:
Measureto optimize for. Will be converted to a
Measurein case it is
character. Initialized to
"classif.ce", i.e. misclassification error for classification and
"regr.mse", i.e. mean squared error for regression.
Optimizerused to find optimal thresholds. If
character, converts to
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
integer(1)Set a temporary log-level for
lgr::get_logger("bbotk"). Initialized to: "warn".
LearnerClassifAvg$new(), id = "classif.avg")(
LearnerRegrAvg$new(), id = "regr.avg")(