LdSvmTrainer Class
Definition
Important
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The IEstimator<TTransformer> to predict a target using a non-linear binary classification model trained with Local Deep SVM.
public sealed class LdSvmTrainer : Microsoft.ML.Trainers.TrainerEstimatorBase<Microsoft.ML.Data.BinaryPredictionTransformer<Microsoft.ML.Trainers.LdSvmModelParameters>,Microsoft.ML.Trainers.LdSvmModelParameters>type LdSvmTrainer = class
inherit TrainerEstimatorBase<BinaryPredictionTransformer<LdSvmModelParameters>, LdSvmModelParameters>Public NotInheritable Class LdSvmTrainer
Inherits TrainerEstimatorBase(Of BinaryPredictionTransformer(Of LdSvmModelParameters), LdSvmModelParameters)- Inheritance
Remarks
To create this trainer, use LdSvm or LdSvm(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 | No |
Training Algorithm Details
Local Deep SVM (LD-SVM) is a generalization of Localized Multiple Kernel Learning for non-linear SVM. Multiple kernel methods learn a different kernel, and hence a different classifier, for each point in the feature space. The prediction time cost for multiple kernel methods can be prohibitively expensive for large training sets because it is proportional to the number of support vectors, and these grow linearly with the size of the training set. LD-SVM reduces the prediction cost by learning a tree-based local feature embedding that is high dimensional and sparse, efficiently encoding non-linearities. Using LD-SVM, the prediction cost grows logarithmically with the size of the training set, rather than linearly, with a tolerable loss in classification accuracy.
Local Deep SVM is an implementation of the algorithm described in C. Jose, P. Goyal, P. Aggrwal, and M. Varma, Local Deep Kernel Learning for Efficient Non-linear SVM Prediction, ICML, 2013.
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 |
Methods
| 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. |