ClassificationModel<T> Class

Represents a statistical model used for classification.

Definition

Namespace: Numerics.NET.DataAnalysis.Models
Assembly: Numerics.NET (in Numerics.NET.dll) Version: 9.1.5

public abstract class ClassificationModel<T> : Model

Visual Basic

Public MustInherit Class ClassificationModel(Of T)
	Inherits Model

C++/CLI

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

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

Inheritance: Object → Model → ClassificationModel<T>

Derived: Numerics.NET.Statistics.LogisticRegressionModel
Numerics.NET.Statistics.Multivariate.LinearDiscriminantAnalysis
Numerics.NET.Statistics.Multivariate.QuadraticDiscriminantAnalysis

Type Parameters

T

Remarks

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 ClassificationModel<T>, you must override FitCore(ModelInput, ParallelOptions).

Constructors

ClassificationModel<T>

Constructs a new classification model based on a model specification.

Properties

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	Gets whether the model has been computed. (Inherited from Model) Obsolete.
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)

Methods

Compute()	Computes the model. (Inherited from Model) Obsolete.
Compute(ParallelOptions)	Computes the model. (Inherited from Model) Obsolete.
Contains	Returns whether another ClassificationModel<T> 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(Matrix<T>, ModelInputFormat)	Predicts the value of the output corresponding to the specified features.
Predict(Vector<T>, ModelInputFormat)	Predicts the value of the output corresponding to the specified input.
PredictCore(Matrix<T>, Boolean)	Predicts the value of the dependent variable based on the specified values of the features.
PredictCore(Vector<T>, 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(Matrix<T>, ModelInputFormat)	Predicts the value of the output corresponding to the specified features.
PredictProbabilities(Vector<T>, ModelInputFormat)	Predicts the value of the output corresponding to the specified input.
PredictProbabilitiesCore(Matrix<T>, Matrix<Double>, Boolean)	Predicts the probabilities of each class based on the specified values of the features.
PredictProbabilitiesCore(Vector<T>, Vector<Double>, Boolean)	Predicts the probabilities of each class based on the specified values of the features.
PredictProbabilitiesInto(IDataFrame, Matrix<Double>, ModelInputFormat)	Predicts the value of the output corresponding to the specified input.
PredictProbabilitiesInto(Matrix<T>, Matrix<Double>, ModelInputFormat)	Predicts the value of the output corresponding to the specified features.
PredictProbabilitiesInto(Vector<T>, Vector<Double>, ModelInputFormat)	Predicts the probabilities of each class based on the specified features.
ResetComputation	Clears all fitted model parameters. (Inherited from Model) Obsolete.
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)