## Definition

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)
Inheritance

## Remarks

### 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

Is normalization required? Yes
Is caching required? No
Required NuGet in addition to Microsoft.ML None

### 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).

## Fields

 The feature column that the trainer expects. (Inherited from TrainerEstimatorBase) The label column that the trainer expects. Can be null, which indicates that label is not used for training. (Inherited from TrainerEstimatorBase) The weight column that the trainer expects. Can be null, which indicates that weight is not used for training. (Inherited from TrainerEstimatorBase)

## Properties

 (Inherited from OnlineLinearTrainer)

## Methods

 Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase) Continues the training of a OnlineLinearTrainer using an already trained modelParameters and returns a ITransformer. (Inherited from OnlineLinearTrainer) (Inherited from TrainerEstimatorBase)

## Extension Methods

 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 are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain 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.