ApproximatedKernelMappingEstimator Class
Definition
Important
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Maps vector columns to a low -dimensional feature space.
public sealed class ApproximatedKernelMappingEstimator : Microsoft.ML.IEstimator<Microsoft.ML.Transforms.ApproximatedKernelTransformer>type ApproximatedKernelMappingEstimator = class
interface IEstimator<ApproximatedKernelTransformer>Public NotInheritable Class ApproximatedKernelMappingEstimator
Implements IEstimator(Of ApproximatedKernelTransformer)- Inheritance
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ApproximatedKernelMappingEstimator
- Implements
Remarks
Estimator Characteristics
| Does this estimator need to look at the data to train its parameters? | Yes |
| Input column data type | Known-sized vector of Single |
| Output column data type | Known-sized vector of Single |
| Exportable to ONNX | No |
The resulting ApproximatedKernelTransformer creates a new column, named as specified in the output column name parameters, where each input vector is mapped to a feature space where inner products approximate one of two shift-invariant kernel functions: The Gaussian kernel, or the Laplacian kernel. By mapping features to a space that approximate non-linear kernels, linear methods can be used to approximate more complex kernel SVM models. This mapping is based on the paper Random Features for Large-Scale Kernel Machines by Rahimi and Recht.
Check the See Also section for links to usage examples.
Methods
| Fit(IDataView) |
Trains and returns a ApproximatedKernelTransformer. |
| GetOutputSchema(SchemaShape) |
Returns the SchemaShape of the schema which will be produced by the transformer. Used for schema propagation and verification in a pipeline. |
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. |