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SymbolicSgdLogisticRegressionBinaryTrainer.Options Class

  • Reference

Definition

Namespace:
Microsoft.ML.Trainers
Assembly:
Microsoft.ML.Mkl.Components.dll
Package:
Microsoft.ML.Mkl.Components v3.0.1
Package:
Microsoft.ML.Mkl.Components v1.0.0
Package:
Microsoft.ML.Mkl.Components v1.1.0
Package:
Microsoft.ML.Mkl.Components v1.2.0
Package:
Microsoft.ML.Mkl.Components v1.3.1
Package:
Microsoft.ML.Mkl.Components v1.4.0
Package:
Microsoft.ML.Mkl.Components v1.5.5
Package:
Microsoft.ML.Mkl.Components v1.6.0
Package:
Microsoft.ML.Mkl.Components v1.7.0
Package:
Microsoft.ML.Mkl.Components v2.0.0

Options for the SymbolicSgdLogisticRegressionBinaryTrainer as used in SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options).

public sealed class SymbolicSgdLogisticRegressionBinaryTrainer.Options : Microsoft.ML.Trainers.TrainerInputBaseWithLabel
type SymbolicSgdLogisticRegressionBinaryTrainer.Options = class
    inherit TrainerInputBaseWithLabel
Public NotInheritable Class SymbolicSgdLogisticRegressionBinaryTrainer.Options
Inherits TrainerInputBaseWithLabel
Inheritance
Object
TrainerInputBase
TrainerInputBaseWithLabel
SymbolicSgdLogisticRegressionBinaryTrainer.Options

Constructors

SymbolicSgdLogisticRegressionBinaryTrainer.Options()

Fields

FeatureColumnName

Column to use for features.

(Inherited from TrainerInputBase)
L2Regularization

L2 regularization.
LabelColumnName

Column to use for labels.

(Inherited from TrainerInputBaseWithLabel)
LearningRate

Learning rate. A larger value can potentially reduce the training time but incur numerical instability and over-fitting.
MemorySize

The acceleration memory budget in MB.
NumberOfIterations

Number of passes over the data.
NumberOfThreads

Degree of lock-free parallelism. Determinism not guaranteed if this is set to higher than 1. The default value is the number of logical cores that are available on the system.
PositiveInstanceWeight

Apply weight to the positive class, for imbalanced data.
Shuffle

Set to true causes the data to shuffle.
Tolerance

Tolerance for difference in average loss in consecutive passes. If the reduction on loss is smaller than the specified tolerance in one iteration, the training process will be terminated.
UpdateFrequency

The number of iterations each thread learns a local model until combining it with the global model. Low value means more updated global model and high value means less cache traffic.

Applies to