MklComponentsCatalog Class
Definition
Important
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Collection of extension methods for RegressionCatalog.RegressionTrainers, BinaryClassificationCatalog.BinaryClassificationTrainers, and TransformsCatalog to create MKL (Math Kernel Library) trainer and transform components.
public static class MklComponentsCatalogtype MklComponentsCatalog = classPublic Module MklComponentsCatalog- Inheritance
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MklComponentsCatalog
Methods
| Ols(RegressionCatalog+RegressionTrainers, OlsTrainer+Options) |
Create OlsTrainer with advanced options, which predicts a target using a linear regression model. |
| Ols(RegressionCatalog+RegressionTrainers, String, String, String) |
Create OlsTrainer, which predicts a target using a linear regression model. |
| SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, Int32) |
Create SymbolicSgdLogisticRegressionBinaryTrainer, which predicts a target using a linear binary classification model trained over boolean label data. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function. The SymbolicSgdLogisticRegressionBinaryTrainer parallelizes SGD using symbolic execution. |
| SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options) |
Create SymbolicSgdLogisticRegressionBinaryTrainer with advanced options, which predicts a target using a linear binary classification model trained over boolean label data. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function. The SymbolicSgdLogisticRegressionBinaryTrainer parallelizes SGD using symbolic execution. |
| VectorWhiten(TransformsCatalog, String, String, WhiteningKind, Single, Int32, Int32) |
Takes column filled with a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix, meaning that they are uncorrelated and each have variance 1. |