EnsembleBinaryClassifier EnsembleBinaryClassifier EnsembleBinaryClassifier Class


Train binary ensemble.

public sealed class EnsembleBinaryClassifier : Microsoft.ML.ILearningPipelineItem, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel
type EnsembleBinaryClassifier = class
    interface CommonInputs.ITrainerInputWithLabel
    interface CommonInputs.ITrainerInput
    interface ILearningPipelineItem
Public NotInheritable Class EnsembleBinaryClassifier
Implements CommonInputs.ITrainerInputWithLabel, ILearningPipelineItem


EnsembleBinaryClassifier() EnsembleBinaryClassifier() EnsembleBinaryClassifier()


BatchSize BatchSize BatchSize

Batch size

Caching Caching Caching

Whether learner should cache input training data

FeatureColumn FeatureColumn FeatureColumn

Column to use for features

LabelColumn LabelColumn LabelColumn

Column to use for labels

NormalizeFeatures NormalizeFeatures NormalizeFeatures

Normalize option for the feature column

NumModels NumModels NumModels

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 OutputCombiner OutputCombiner

Output combiner

SamplingType SamplingType SamplingType

Sampling Type

ShowMetrics ShowMetrics ShowMetrics

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 SubModelSelectorType SubModelSelectorType

Algorithm to prune the base learners for selective Ensemble

TrainingData TrainingData TrainingData

The data to be used for training

TrainParallel TrainParallel TrainParallel

All the base learners will run asynchronously if the value is true


ApplyStep(ILearningPipelineStep, Experiment) ApplyStep(ILearningPipelineStep, Experiment) ApplyStep(ILearningPipelineStep, Experiment)
GetInputData() GetInputData() GetInputData()

Applies to