LinearSvmTrainer Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.StandardTrainers.dll

Package:

Microsoft.ML v5.0.0-preview.1.25125.4

Source:

LinearSvm.cs

The IEstimator<TTransformer> to predict a target using a linear binary classification model trained with Linear SVM.

C#

public sealed class LinearSvmTrainer : Microsoft.ML.Trainers.OnlineLinearTrainer<Microsoft.ML.Data.BinaryPredictionTransformer<Microsoft.ML.Trainers.LinearBinaryModelParameters>,Microsoft.ML.Trainers.LinearBinaryModelParameters>

Inheritance

Object

TrainerEstimatorBase<TTransformer,TModel>

OnlineLinearTrainer<BinaryPredictionTransformer<LinearBinaryModelParameters>,LinearBinaryModelParameters>

LinearSvmTrainer

Remarks

To create this trainer, use LinearSvm or LinearSvm(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

Linear SVM implements an algorithm that finds a hyperplane in the feature space for binary classification, by solving an SVM problem. For instance, with feature values $f_0,f_1,...,f_{D-1}$, the prediction is given by determining what side of the hyperplane the point falls into. That is the same as the sign of the feautures' weighted sum, i.e. $\sum _{i=0}^{D-1}(w_i\ast f_i)+b$, where $w_0,w_1,...,w_{D-1}$ are the weights computed by the algorithm, and $b$ is the bias computed by the algorithm.

Linear SVM implements the PEGASOS method, which alternates between stochastic gradient descent steps and projection steps, introduced in this paper by Shalev-Shwartz, Singer and Srebro.

Check the See Also section for links to usage examples.

Fields

FeatureColumn	The feature column that the trainer expects. (Inherited from TrainerEstimatorBase<TTransformer,TModel>)
LabelColumn	The label column that the trainer expects. Can be null, which indicates that label is not used for training. (Inherited from TrainerEstimatorBase<TTransformer,TModel>)
WeightColumn	The weight column that the trainer expects. Can be null, which indicates that weight is not used for training. (Inherited from TrainerEstimatorBase<TTransformer,TModel>)

Properties

Info	(Inherited from OnlineLinearTrainer<TTransformer,TModel>)

Methods

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>)
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.

Applies to

See also

• LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LinearSvmTrainer+Options)
• LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32)