OnlineGradientDescentTrainer Class


The IEstimator<TTransformer> for training a linear regression model using Online Gradient Descent (OGD) for estimating the parameters of the linear regression model.

public sealed class OnlineGradientDescentTrainer : Microsoft.ML.Trainers.AveragedLinearTrainer<Microsoft.ML.Data.RegressionPredictionTransformer<Microsoft.ML.Trainers.LinearRegressionModelParameters>,Microsoft.ML.Trainers.LinearRegressionModelParameters>
type OnlineGradientDescentTrainer = class
    inherit AveragedLinearTrainer<RegressionPredictionTransformer<LinearRegressionModelParameters>, LinearRegressionModelParameters>
Public NotInheritable Class OnlineGradientDescentTrainer
Inherits AveragedLinearTrainer(Of RegressionPredictionTransformer(Of LinearRegressionModelParameters), LinearRegressionModelParameters)


To create this trainer, use OnlineGradientDescent or OnlineGradientDescent(Options).

Input and Output Columns

The input label column data must be Single. 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 predicted by the model.

Trainer Characteristics

Machine learning task Regression
Is normalization required? Yes
Is caching required? No
Required NuGet in addition to Microsoft.ML None
Exportable to ONNX Yes

Training Algorithm Details

Stochastic gradient descent uses a simple yet efficient iterative technique to fit model coefficients using error gradients for convex loss functions. Online Gradient Descent (OGD) implements the standard (non-batch) stochastic gradient descent, with a choice of loss functions, and an option to update the weight vector using the average of the vectors seen over time (averaged argument is set to True by default).

Check the See Also section for links to usage examples.



The feature column that the trainer expects.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)

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

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)

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

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)


Info (Inherited from OnlineLinearTrainer<TTransformer,TModel>)



Trains and returns a ITransformer.

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

Continues the training of a OnlineLinearTrainer<TTransformer,TModel> using an already trained modelParameters and returns a ITransformer.

(Inherited from OnlineLinearTrainer<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.

Applies to

See also