ArmMachineLearningModelFactory.MachineLearningStackEnsembleSettings Method
Definition
Important
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Advances setting to customize StackEnsemble run.
public static Azure.ResourceManager.MachineLearning.Models.MachineLearningStackEnsembleSettings MachineLearningStackEnsembleSettings(BinaryData stackMetaLearnerKWargs = default, double? stackMetaLearnerTrainPercentage = default, Azure.ResourceManager.MachineLearning.Models.MachineLearningStackMetaLearnerType? stackMetaLearnerType = default);static member MachineLearningStackEnsembleSettings : BinaryData * Nullable<double> * Nullable<Azure.ResourceManager.MachineLearning.Models.MachineLearningStackMetaLearnerType> -> Azure.ResourceManager.MachineLearning.Models.MachineLearningStackEnsembleSettingsPublic Shared Function MachineLearningStackEnsembleSettings (Optional stackMetaLearnerKWargs As BinaryData = Nothing, Optional stackMetaLearnerTrainPercentage As Nullable(Of Double) = Nothing, Optional stackMetaLearnerType As Nullable(Of MachineLearningStackMetaLearnerType) = Nothing) As MachineLearningStackEnsembleSettingsParameters
- stackMetaLearnerKWargs
- BinaryData
Optional parameters to pass to the initializer of the meta-learner.
Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.
- stackMetaLearnerType
- Nullable<MachineLearningStackMetaLearnerType>
The meta-learner is a model trained on the output of the individual heterogeneous models.\r\nDefault meta-learners are LogisticRegression for classification tasks (or LogisticRegressionCV if cross-validation is enabled) and ElasticNet for regression/forecasting tasks (or ElasticNetCV if cross-validation is enabled).\r\nThis parameter can be one of the following strings: LogisticRegression, LogisticRegressionCV, LightGBMClassifier, ElasticNet, ElasticNetCV, LightGBMRegressor, or LinearRegression.
Returns
A new MachineLearningStackEnsembleSettings instance for mocking.
