SgdBinaryTrainerBase<TModel> Class
Definition
Important
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public abstract class SgdBinaryTrainerBase<TModel> : Microsoft.ML.Trainers.LinearTrainerBase<Microsoft.ML.Data.BinaryPredictionTransformer<TModel>,TModel> where TModel : classtype SgdBinaryTrainerBase<'Model (requires 'Model : null)> = class
inherit LinearTrainerBase<BinaryPredictionTransformer<'Model>, 'Model (requires 'Model : null)>Public MustInherit Class SgdBinaryTrainerBase(Of TModel)
Inherits LinearTrainerBase(Of BinaryPredictionTransformer(Of TModel), TModel)Type Parameters
- TModel
- Inheritance
-
LinearTrainerBase<BinaryPredictionTransformer<TModel>,TModel>SgdBinaryTrainerBase<TModel>
- Derived
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 |
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. |