MklComponentsCatalog Class

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.Mkl.Components.dll

Package:

Microsoft.ML.Mkl.Components v3.0.1

Collection of extension methods for RegressionCatalog.RegressionTrainers, BinaryClassificationCatalog.BinaryClassificationTrainers, and TransformsCatalog to create MKL (Math Kernel Library) trainer and transform components.

public static class MklComponentsCatalog

type MklComponentsCatalog = class

Public Module MklComponentsCatalog

Inheritance

Object

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.