LinearSvmTrainer Class
Definition
Important
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The IEstimator<TTransformer> to predict a target using a linear binary classification model trained with Linear SVM.
public sealed class LinearSvmTrainer : Microsoft.ML.Trainers.OnlineLinearTrainer<Microsoft.ML.Data.BinaryPredictionTransformer<Microsoft.ML.Trainers.LinearBinaryModelParameters>,Microsoft.ML.Trainers.LinearBinaryModelParameters>type LinearSvmTrainer = class
inherit OnlineLinearTrainer<BinaryPredictionTransformer<LinearBinaryModelParameters>, LinearBinaryModelParameters>Public NotInheritable Class LinearSvmTrainer
Inherits OnlineLinearTrainer(Of BinaryPredictionTransformer(Of LinearBinaryModelParameters), LinearBinaryModelParameters)- Inheritance
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. $\sum_{i = 0}^{D-1} \left(w_i * f_i \right) + b$, 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 |
| WeightColumn |
The weight column that the trainer expects. Can be |
Properties
| Info | (Inherited from OnlineLinearTrainer<TTransformer,TModel>) |
Methods
| Fit(IDataView, LinearModelParameters) |
Continues the training of a OnlineLinearTrainer<TTransformer,TModel> 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. |