SgdNonCalibratedTrainer Class
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
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
-
LinearTrainerBase<BinaryPredictionTransformer<TModel>,TModel>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 |
| 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. |