BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput Class

Base class for performance measurement methods based on splitting the data into multiple sets, such as SplitSetValidationTModel, TInput, TOutput, CrossValidationTModel, TInput, TOutput and BootstrapTModel, TInput, TOutput.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.MachineLearning.PerformanceBaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput
      Accord.MachineLearning.PerformanceBaseSplitSetValidationTResult, TModel, TInput, TOutput
      Accord.MachineLearning.PerformanceCrossValidationTModel, TLearner, TInput, TOutput

Namespace: Accord.MachineLearning.Performance
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

Syntax

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public abstract class BaseSplitSetValidation<TResult, TModel, TLearner, TInput, TOutput> : ParallelLearningBase, 
	ISupervisedLearning<TResult, TInput, TOutput>
where TResult : Object, ITransform<TInput, TOutput>
where TModel : class, Object, ITransform<TInput, TOutput>

Public MustInherit Class BaseSplitSetValidation(Of TResult As {Object, ITransform(Of TInput, TOutput)}, TModel As {Class, Object, ITransform(Of TInput, TOutput)}, TLearner, TInput, TOutput)
	Inherits ParallelLearningBase
	Implements ISupervisedLearning(Of TResult, TInput, TOutput)

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Type Parameters

TResult: The type of the result learned by the validation method (e.g. CrossValidationResultTModel, TInput, TOutput).
TModel: The type of the machine learning model.
TLearner: The type of the learning algorithm used to learn TModel.
TInput: The type of the input data.
TOutput: The type of the output data or labels.

The BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput type exposes the following members.

Constructors

	Name	Description
	BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput	Initializes a new instance of the BaseSplitSetValidationTResult, TModel, TInput, TOutput class.

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Properties

	Name	Description
	DefaultValue	Gets or sets a value to be used as the Loss in case the model throws an exception during learning. Default is null (exceptions will not be ignored).
	Fit	Gets or sets a LearnNewModelTLearner, TInput, TOutput, TModel function that can be used to create new machine learning models using the current learning algorithm.
	Learner	Gets or sets a CreateLearnerFromSubsetTLearner, TInput, TOutput function that can be used to create a TModel from a subset of the learning dataset.
	Loss	Gets or sets a ComputeLossTOutput, TInfo function that can be used to measure how far the actual model predictions were from the expected ground-truth.
	ParallelOptions	Gets or sets the parallelization options for this algorithm. (Inherited from ParallelLearningBase.)
	Token	Gets or sets a cancellation token that can be used to cancel the algorithm while it is running. (Inherited from ParallelLearningBase.)

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Methods

	Name	Description
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	Learn	Learns a model that can map the given inputs to the given outputs.
	LearnSubset	Learns and evaluates a model in a single subset of the data.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

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Reference

Accord.MachineLearning.Performance Namespace

BaseSplitSetValidationAddLanguageSpecificTextSet("LSTD8F37857_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TResult, TModel, TLearner, TInput, TOutputAddLanguageSpecificTextSet("LSTD8F37857_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

BaseSplitSetValidationTResult, TModel, TLearner, TInput, TOutput Class