SgdCalibratedTrainer Class
Definition
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
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 knownsized 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 
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 stateoftheart performance. This trainer implements the Hogwild Stochastic Gradient Descent for binary classification that supports multithreading 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) 
Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase<TTransformer,TModel>) 
Fit(IDataView, LinearModelParameters) 
Continues the training of a SdcaLogisticRegressionBinaryTrainer using an already trained 
GetOutputSchema(SchemaShape)  (Inherited from TrainerEstimatorBase<TTransformer,TModel>) 
Extension Methods
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
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