Parameters

• weights :: numeric
Relative weights of input predictions. If this has length 1, it is ignored and weighs all inputs equally. Otherwise it must have length equal to the number of connected inputs. Initialized to 1 (equal weights).

The commonality of ensemble methods using PipeOpEnsemble is that they take a NULL-input during training and save an empty $state. They can be used following a set of PipeOpLearner PipeOps to perform (possibly weighted) prediction averaging. See e.g. PipeOpClassifAvg and PipeOpRegrAvg which both inherit from this class. Should it be necessary to use the output of preceding Learners during the "training" phase, then PipeOpEnsemble should not be used. In fact, if training time behaviour of a Learner is important, then one should use a PipeOpLearnerCV instead of a PipeOpLearner, and the ensemble can be created with a Learner encapsulated by a PipeOpLearner. See LearnerClassifAvg and LearnerRegrAvg for examples. Fields Only fields inherited from PipeOp. Methods Methods inherited from PipeOp as well as: • weighted_avg_prediction(inputs, weights, row_ids, truth) (list of Prediction, numeric, integer | character, list) -> NULL Create Predictions that correspond to the weighted average of incoming Predictions. This is called by private$.predict() with cleaned and sanity-checked values: inputs are guaranteed to fit together, row_ids and truth are guaranteed to be the same as each one in inputs, and weights is guaranteed to have the same length as inputs.
This method is abstract, it must be implemented by deriving classes.