Skip to contents

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.





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

  • LearnerRegrAvg$new(), id = "regr.avg")
    (chr) -> self


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