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  • Extreme.Statistics
    • Aggregator Class
    • AnovaModel Class
    • AnovaModelRow Class
    • AnovaRow Class
    • AnovaRowCollection Class
    • AnovaRowType Enumeration
    • AnovaTable Class
    • BoundaryIntervalBehavior Enumeration
    • CategoricalScale Class
    • CategoricalVariable Class
    • CategoricalVariable.CategoricalFilters Structure
    • Cell Structure
    • CellArray Class
    • ClassificationModel Class
    • ClusteringModel Class
    • CollectionSortOrder Class
    • ContingencyTable Class
    • ContingencyTableCell Structure
    • DataArray(T) Class
    • DataArrayElement(T) Class
    • DateTimeInterval Structure
    • DateTimeScale Class
    • DateTimeUnit Enumeration
    • DateTimeVariable Class
    • DateTimeVariable.DateTimeFilters Structure
    • Descriptives(T) Class
    • Filter Class
    • GeneralizedLinearModel Class
    • Histogram Class
    • HistogramBin Structure
    • HistogramBinCollection Class
    • HypothesisTests Class
    • InsufficientDataException Class
    • ITransformationModel Interface
    • Kernel Class
    • KernelDensity Class
    • KernelDensityBandwidthEstimator Enumeration
    • KeyVariable Class
    • KeyVariable(T) Class
    • LinearRegressionModel Class
    • LinkFunction Class
    • LogisticRegressionMethod Enumeration
    • LogisticRegressionModel Class
    • MissingValueAction Enumeration
    • MissingValueException Class
    • Model Class
    • ModelExtensions Class
    • ModelFamily Class
    • ModelFitOptions Class
    • ModelInput Class
    • ModelInputCategory Enumeration
    • ModelInputFormat Enumeration
    • ModelInputGroup Class
    • ModelSerialization Enumeration
    • ModelStatus Enumeration
    • ModelTerm Class
    • ModelTermCollection Class
    • ModelTermKind Enumeration
    • MultipleMissingValueAction Enumeration
    • NearestCorrelationMatrixAlgorithm Enumeration
    • NonlinearRegressionModel Class
    • NumericalScale Class
    • NumericalVariable Class
    • NumericalVariable.NumericalFilters Structure
    • NumericalVariable.NumericalVariableTransforms Structure
    • Observation Structure
    • ObservationCollection Class
    • OneWayAnovaModel Class
    • OneWayRAnovaModel Class
    • Parameter Class
    • ParameterCollection Class
    • ParameterVector Class
    • PolynomialRegressionModel Class
    • RankTiebreaker Enumeration
    • RegressionModel Class
    • RegularizedRegressionModel Class
    • ScaleFittingMethod Enumeration
    • SeriesExtensions Class
    • SimpleRegressionKind Enumeration
    • SimpleRegressionModel Class
    • SortOrder Enumeration
    • SpecialBins Enumeration
    • Stats Class
    • StepwiseCriterion Enumeration
    • StepwiseOptions Class
    • StepwiseRegressionMethod Enumeration
    • SumsOfSquaresType Enumeration
    • TestOfHomogeneityOfVariances Enumeration
    • TestOfNormality Enumeration
    • TransformationModel Class
    • TransformedParameter Class
    • TwoWayAnovaModel Class
    • UnivariateModel Class
    • Variable Class
    • VariableCollection Class
    • WindowFilter Class
  • ClassificationModel Class
    • ClassificationModel Constructor
    • Properties
    • Methods
ClassificationModel ClassExtreme Optimization Numerical Libraries for .NET Professional
Represents a statistical model used for classification.
Inheritance Hierarchy

SystemObject
  Extreme.StatisticsModel
    Extreme.StatisticsClassificationModel
      Extreme.StatisticsLogisticRegressionModel
      Extreme.Statistics.MultivariateLinearDiscriminantAnalysis

Namespace: Extreme.Statistics
Assembly: Extreme.Numerics.Net40 (in Extreme.Numerics.Net40.dll) Version: 6.0.16073.0 (6.0.17114.0)
Syntax

C#
VB
C++
F#
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public abstract class ClassificationModel : Model
Public MustInherit Class ClassificationModel
	Inherits Model
public ref class ClassificationModel abstract : public Model
[<AbstractClassAttribute>]
type ClassificationModel =  
    class
        inherit Model
    end

The ClassificationModel type exposes the following members.

Constructors

  NameDescription
Protected methodClassificationModel
Constructs a new classification model based on a model specification.
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Properties

  NameDescription
Public propertyBaseFeatureIndex
Gets an index containing the keys of the columns that are required inputs to the model.
(Inherited from Model.)
Public propertyCanPredictProbabilities
Gets whether the classifier supports predicting probabilities for each class.
Public propertyCategoryIndex
Gets the category index of the dependent variable or targets.
Public propertyComputed
Gets whether the regression model has been computed.
(Inherited from Model.)
Public propertyData
Gets an object that contains all the data used as input to the model.
(Inherited from Model.)
Public propertyDependentVariable
Gets a vector that contains the dependent variable that is to be fitted.
Public propertyIndependentVariables
Gets a matrix whose columns contain the independent variables in the model.
Public propertyInputSchema
Gets the schema for the features used for fitting the model.
(Inherited from Model.)
Public propertyLogLikelihood
Gets the log-likelihood of the fitted model.
Public propertyMaxDegreeOfParallelism
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model.)
Public propertyModelSchema
Gets the collection of variables used in the model.
(Inherited from Model.)
Protected propertyModelSubset
Returns an array of indexes of final model features in the set of input features.
(Inherited from Model.)
Public propertyNumberOfObservations
Gets the number of observations the model is based on.
(Inherited from Model.)
Protected propertyParallelOptions
Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model.)
Public propertyParameterValues
Gets the collection of parameters associated with this model.
Public propertyPredictedLogProbabilities
Gets a vector containing the model's predicted values for the dependent variable.
Public propertyPredictedProbabilities
Gets a vector containing the model's predicted values for the dependent variable.
Public propertyPredictions
Gets a vector containing the model's predicted values for the dependent variable.
Public propertyProbabilityResiduals
Gets a matrix containing the residuals of the model.
Public propertyStatus
Gets the status of the model, which determines which information is available.
(Inherited from Model.)
Public propertySupportsWeights
Indicates whether the model supports case weights.
(Inherited from Model.)
Public propertyWeights
Gets or sets the actual weights.
(Inherited from Model.)
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Methods

  NameDescription
Public methodCompute
Computes the model.
(Inherited from Model.)
Public methodCompute(ParallelOptions)
Computes the model.
(Inherited from Model.)
Protected methodComputeModel
Computes the model.
(Inherited from Model.)
Protected methodComputeModel(ParallelOptions)
Fits the model to the data.
(Inherited from Model.)
Protected methodComputeModel(ModelInput, ParallelOptions)
Computes the model using the specified parallelization options.
(Inherited from Model.)
Public methodContains
Returns whether another ClassificationModel is nested within this instance.
Public methodEquals
Determines whether the specified Object is equal to the current Object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodFit
Fits the model to the data.
(Inherited from Model.)
Public methodFit(ParallelOptions)
Fits the model to the data.
(Inherited from Model.)
Protected methodFitCore
Computes the model to the specified input using the specified parallelization options.
(Inherited from Model.)
Public methodGetHashCode
Serves as a hash function for a particular type.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodPredict(IDataFrame, ModelInputFormat)
Predicts the most likely class based on the specified features.
Public methodPredict(MatrixDouble, ModelInputFormat)
Predicts the value of the output corresponding to the specified features.
Public methodPredict(VectorDouble, ModelInputFormat)
Predicts the value of the output corresponding to the specified input.
Protected methodPredictCore(MatrixDouble, Boolean, Boolean)
Predicts the value of the dependent variable based on the specified values of the features.
Protected methodPredictCore(VectorDouble, Boolean, Boolean)
Predicts the class based on the specified values of the features.
Public methodPredictProbabilities(IDataFrame, ModelInputFormat)
Predicts the value of the output corresponding to the specified input.
Public methodPredictProbabilities(MatrixDouble, ModelInputFormat)
Predicts the value of the output corresponding to the specified features.
Public methodPredictProbabilities(VectorDouble, ModelInputFormat)
Predicts the value of the output corresponding to the specified input.
Protected methodPredictProbabilitiesCore(MatrixDouble, MatrixDouble, Boolean, Boolean)
Predicts the probabilities of each class based on the specified values of the features.
Protected methodPredictProbabilitiesCore(VectorDouble, VectorDouble, Boolean, Boolean)
Predicts the probabilities of each class based on the specified values of the features.
Public methodPredictProbabilitiesInto(IDataFrame, MatrixDouble, ModelInputFormat)
Predicts the value of the output corresponding to the specified input.
Public methodPredictProbabilitiesInto(MatrixDouble, MatrixDouble, ModelInputFormat)
Predicts the value of the output corresponding to the specified features.
Public methodPredictProbabilitiesInto(VectorDouble, VectorDouble, ModelInputFormat)
Predicts the probabilities of each class based on the specified features.
Public methodResetComputation
Clears all computed model parameters.
(Inherited from Model.)
Public methodSetDataSource
Uses the specified data frame as the source for all input variables.
(Inherited from Model.)
Public methodSummarize
Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model.)
Public methodSummarize(SummaryOptions)
Returns a string containing a human-readable summary of the object using the specified options.
(Inherited from Model.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Model.)
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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:

ClassDescription
LogisticRegressionModelA binary or multinomial logistic regression model with one or more independent variables.
LinearDiscriminantAnalysisA linear discriminant analysis model with multiple independent variables.

Note to inheritors: When you inherit from ClassificationModel, you must override ComputeModel.

Version Information

Numerical Libraries

Supported in: 6.0
See Also

Reference

Extreme.Statistics Namespace

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