SgdCalibratedTrainer Class
Definition
Important
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The IEstimator<TTransformer> for training logistic regression using a parallel stochastic gradient method. The trained model is calibrated and can produce probability by feeding the output value of the linear function to a PlattCalibrator.
public sealed class SgdCalibratedTrainer : Microsoft.ML.Trainers.SgdBinaryTrainerBase<Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.LinearBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>type SgdCalibratedTrainer = class
inherit SgdBinaryTrainerBase<CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>>Public NotInheritable Class SgdCalibratedTrainer
Inherits SgdBinaryTrainerBase(Of CalibratedModelParametersBase(Of LinearBinaryModelParameters, PlattCalibrator))- Inheritance
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LinearTrainerBase<BinaryPredictionTransformer<TModel>,TModel>SgdCalibratedTrainer
Remarks
To create this trainer, use SgdCalibrated or SgdCalibrated(Options).
Input and Output Columns
The input label column data must be Boolean. The input features column data must be a known-sized vector of Single.
This trainer outputs the following columns:
| Output Column Name | Column Type | Description | |
|---|---|---|---|
Score |
Single | The unbounded score that was calculated by the model. | |
PredictedLabel |
Boolean | The predicted label, based on the sign of the score. A negative score maps to false and a positive score maps to true. |
|
Probability |
Single | The probability calculated by calibrating the score of having true as the label. Probability value is in range [0, 1]. |
Trainer Characteristics
| Machine learning task | Binary classification |
| Is normalization required? | Yes |
| Is caching required? | No |
| Required NuGet in addition to Microsoft.ML | None |
| Exportable to ONNX | Yes |
Training Algorithm Details
The Stochastic Gradient Descent (SGD) is one of the popular stochastic optimization procedures that can be integrated into several machine learning tasks to achieve state-of-the-art performance. This trainer implements the Hogwild Stochastic Gradient Descent for binary classification that supports multi-threading without any locking. If the associated optimization problem is sparse, Hogwild Stochastic Gradient Descent achieves a nearly optimal rate of convergence. For more details about Hogwild Stochastic Gradient Descent can be found here.
Check the See Also section for links to examples of the usage.
Fields
| FeatureColumn |
The feature column that the trainer expects. (Inherited from TrainerEstimatorBase<TTransformer,TModel>) |
| LabelColumn |
The label column that the trainer expects. Can be |
| WeightColumn |
The weight column that the trainer expects. Can be |
Properties
| Info | (Inherited from SgdBinaryTrainerBase<TModel>) |
Methods
| Fit(IDataView, LinearModelParameters) |
Continues the training of a SdcaLogisticRegressionBinaryTrainer using an already trained |
| Fit(IDataView) |
Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase<TTransformer,TModel>) |
| GetOutputSchema(SchemaShape) | (Inherited from TrainerEstimatorBase<TTransformer,TModel>) |
Extension Methods
| AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment) |
Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes. |
| WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>) |
Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called. |