Parallel Ensemble (bagging, stacking, etc)ΒΆ

The documentation is generated based on the sources available at dotnet/machinelearning and released under MIT License.

Type: binaryclassifiertrainer Aliases: WeightedEnsemble, pe, ParallelEnsemble Namespace: Microsoft.ML.Runtime.Ensemble Assembly: Microsoft.ML.Ensemble.dll Microsoft Documentation: Parallel Ensemble (bagging, stacking, etc)


A generic ensemble classifier for binary classification.


Name Short name Default Description
basePredictors bp Microsoft. ML. Runtime. IComponentFactory`1[Microsoft. ML. Trainers. Online. LinearSvm][] Base predictor type
batchSize bs -1 Batch size
numModels nm   Number of models per batch. If not specified, will default to 50 if there is only one base predictor, or the number of base predictors otherwise.
outputCombiner oc Microsoft. ML. Ensemble. EntryPoints. MedianFactory Output combiner
samplingType st BootstrapSelector.Arguments Sampling Type
showMetrics sm False True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set
subModelSelectorType pt Microsoft. ML. Ensemble. EntryPoints. AllSelectorFactory Algorithm to prune the base learners for selective Ensemble
trainParallel tp False All the base learners will run asynchronously if the value is true