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ClassificationModelT Class

Extreme Optimization Numerical Libraries for .NET Professional

Represents a statistical model used for classification.

Inheritance Hierarchy

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SystemObject
  Extreme.DataAnalysis.ModelsModel
    Extreme.DataAnalysis.ModelsClassificationModelT
      Extreme.StatisticsLogisticRegressionModel
      Extreme.Statistics.MultivariateLinearDiscriminantAnalysis

Namespace:  Extreme.DataAnalysis.Models
Assembly:  Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1

Syntax

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C#

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public abstract class ClassificationModel<T> : Model

Public MustInherit Class ClassificationModel(Of T)
	Inherits Model

generic<typename T>
public ref class ClassificationModel abstract : public Model

[<AbstractClassAttribute>]
type ClassificationModel<'T> =  
    class
        inherit Model
    end

Type Parameters

T

The ClassificationModelT type exposes the following members.

Constructors

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	Name	Description
	ClassificationModelT	Constructs a new classification model based on a model specification.

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Properties

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	Name	Description
	BaseFeatureIndex	Gets an index containing the keys of the columns that are required inputs to the model. (Inherited from Model.)
	CanPredictProbabilities	Gets whether the classifier supports predicting probabilities for each class.
	CategoryIndex	Gets the category index of the dependent variable or targets.
	Computed	Obsolete. Gets whether the model has been computed. (Inherited from Model.)
	Data	Gets an object that contains all the data used as input to the model. (Inherited from Model.)
	DependentVariable	Gets a vector that contains the dependent variable that is to be fitted.
	Fitted	Gets whether the model has been computed. (Inherited from Model.)
	IndependentVariables	Gets a matrix whose columns contain the independent variables in the model.
	InputSchema	Gets the schema for the features used for fitting the model. (Inherited from Model.)
	MaxDegreeOfParallelism	Gets or sets the maximum degree of parallelism enabled by this instance. (Inherited from Model.)
	ModelSchema	Gets the collection of variables used in the model. (Inherited from Model.)
	NumberOfObservations	Gets the number of observations the model is based on. (Inherited from Model.)
	ParallelOptions	Gets or sets an object that specifies how the calculation of the model should be parallelized. (Inherited from Model.)
	PredictedLogProbabilities	Gets a vector containing the model's predicted values for the dependent variable.
	PredictedProbabilities	Gets a vector containing the model's predicted values for the dependent variable.
	Predictions	Gets a vector containing the model's predicted values for the dependent variable.
	ProbabilityResiduals	Gets a matrix containing the residuals of the model.
	Status	Gets the status of the model, which determines which information is available. (Inherited from Model.)
	SupportsWeights	Indicates whether the model supports case weights. (Inherited from Model.)
	Weights	Gets or sets the actual weights. (Inherited from Model.)

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Methods

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	Name	Description
	Compute	Obsolete. Computes the model. (Inherited from Model.)
	Compute(ParallelOptions)	Obsolete. Computes the model. (Inherited from Model.)
	Contains	Returns whether another ClassificationModelT is nested within this instance.
	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.)
	Fit	Fits the model to the data. (Inherited from Model.)
	Fit(ParallelOptions)	Fits the model to the data. (Inherited from Model.)
	FitCore	Computes the model to the specified input using the specified parallelization options. (Inherited from Model.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Predict(IDataFrame, ModelInputFormat)	Predicts the most likely class based on the specified features.
	Predict(MatrixT, ModelInputFormat)	Predicts the value of the output corresponding to the specified features.
	Predict(VectorT, ModelInputFormat)	Predicts the value of the output corresponding to the specified input.
	PredictCore(MatrixT, Boolean)	Predicts the value of the dependent variable based on the specified values of the features.
	PredictCore(VectorT, Boolean)	Predicts the class based on the specified values of the features.
	PredictProbabilities(IDataFrame, ModelInputFormat)	Predicts the value of the output corresponding to the specified input.
	PredictProbabilities(MatrixT, ModelInputFormat)	Predicts the value of the output corresponding to the specified features.
	PredictProbabilities(VectorT, ModelInputFormat)	Predicts the value of the output corresponding to the specified input.
	PredictProbabilitiesCore(MatrixT, MatrixDouble, Boolean)	Predicts the probabilities of each class based on the specified values of the features.
	PredictProbabilitiesCore(VectorT, VectorDouble, Boolean)	Predicts the probabilities of each class based on the specified values of the features.
	PredictProbabilitiesInto(IDataFrame, MatrixDouble, ModelInputFormat)	Predicts the value of the output corresponding to the specified input.
	PredictProbabilitiesInto(MatrixT, MatrixDouble, ModelInputFormat)	Predicts the value of the output corresponding to the specified features.
	PredictProbabilitiesInto(VectorT, VectorDouble, ModelInputFormat)	Predicts the probabilities of each class based on the specified features.
	ResetComputation	Obsolete. Clears all fitted model parameters. (Inherited from Model.)
	ResetFit	Clears all fitted model parameters. (Inherited from Model.)
	SetDataSource	Uses the specified data frame as the source for all input variables. (Inherited from Model.)
	Summarize	Returns a string containing a human-readable summary of the object using default options. (Inherited from Model.)
	Summarize(SummaryOptions)	Returns a string containing a human-readable summary of the object using the specified options. (Inherited from Model.)
	ToString	Returns a string that represents the current object. (Inherited from Model.)

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Remarks

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This is an abstract base class and cannot be instantiated directly. Instead, use one of the inherited types, as listed in the table below:

Class	Description
LogisticRegressionModel	A binary or multinomial logistic regression model with one or more independent variables.
LinearDiscriminantAnalysis	A linear discriminant analysis model with multiple independent variables.

Note to inheritors: When you inherit from ClassificationModelT, you must override FitCore(ModelInput, ParallelOptions).

See Also

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Reference

Extreme.DataAnalysis.Models Namespace

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