SgdNonCalibratedTrainer Class

  • Reference

Definition

Namespace:
Microsoft.ML.Trainers
Assembly:
Microsoft.ML.StandardTrainers.dll
Package:
Microsoft.ML v3.0.1
Package:
Microsoft.ML v1.0.0
Package:
Microsoft.ML v1.1.0
Package:
Microsoft.ML v1.2.0
Package:
Microsoft.ML v1.3.1
Package:
Microsoft.ML v1.4.0
Package:
Microsoft.ML v1.5.5
Package:
Microsoft.ML v1.6.0
Package:
Microsoft.ML v1.7.0
Package:
Microsoft.ML v2.0.0

The IEstimator<TTransformer> for training logistic regression using a parallel stochastic gradient method.

public sealed class SgdNonCalibratedTrainer : Microsoft.ML.Trainers.SgdBinaryTrainerBase<Microsoft.ML.Trainers.LinearBinaryModelParameters>
type SgdNonCalibratedTrainer = class
    inherit SgdBinaryTrainerBase<LinearBinaryModelParameters>
Public NotInheritable Class SgdNonCalibratedTrainer
Inherits SgdBinaryTrainerBase(Of LinearBinaryModelParameters)
Inheritance
Object
TrainerEstimatorBase<TTransformer,TModel>
LinearTrainerBase<BinaryPredictionTransformer<TModel>,TModel>
SgdBinaryTrainerBase<LinearBinaryModelParameters>
SgdNonCalibratedTrainer

Remarks

To create this trainer, use SgdNonCalibrated or SgdNonCalibrated(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.

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 null, which indicates that label is not used for training.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
WeightColumn

The weight column that the trainer expects. Can be null, which indicates that weight is not used for training.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)

Properties

Info (Inherited from SgdBinaryTrainerBase<TModel>)

Methods

Fit(IDataView)

Trains and returns a ITransformer.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
Fit(IDataView, LinearModelParameters)

Continues the training of a SdcaLogisticRegressionBinaryTrainer using an already trained modelParameters and returns a Microsoft.ML.Data.BinaryPredictionTransformer.

(Inherited from SgdBinaryTrainerBase<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.

Applies to

See also