LogisticRegressionModel Class

public class LogisticRegressionModel : ClassificationModel<double>

Public Class LogisticRegressionModel
	Inherits ClassificationModel(Of Double)

public ref class LogisticRegressionModel : public ClassificationModel<double>

type LogisticRegressionModel =  
    class
        inherit ClassificationModel<float>
    end

The LogisticRegressionModel type exposes the following members.

	Name	Description
	LogisticRegressionModel(ICategoricalVector, VectorDouble)	Constructs a new LogisticRegressionModel.
	LogisticRegressionModel(IDataFrame, String)	Constructs a new LogisticRegressionModel.
	LogisticRegressionModel(IDataFrame, String, String)	Constructs a new LogisticRegressionModel.
	LogisticRegressionModel(VectorDouble, MatrixDouble, VectorDouble)	Constructs a new SimpleRegressionModel.
	LogisticRegressionModel(ICategoricalVector, VectorDouble, VectorDouble, LogisticRegressionMethod)	Constructs a new LogisticRegressionModel.
	LogisticRegressionModel(ModelInput, IIndex, VectorDouble, SymmetricMatrixDouble, Int32, Double)	Constructs a fitted logistic regression model.

	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. (Inherited from ClassificationModelT.)
	CategoryIndex	Gets the category index of the dependent variable or targets. (Inherited from ClassificationModelT.)
	Computed	Obsolete. Gets whether the model has been computed. (Inherited from Model.)
	ConvergenceStatus	Gets the convergence status of the algorithm that computes the model parameters.
	CovarianceMatrix	Obsolete. Gets the covariance matrix of the model parameters.
	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. (Inherited from ClassificationModelT.)
	Fitted	Gets whether the model has been computed. (Inherited from Model.)
	IndependentVariables	Gets a matrix whose columns contain the independent variables in the model. (Inherited from ClassificationModelT.)
	InputSchema	Gets the schema for the features used for fitting the model. (Inherited from Model.)
	LogLikelihood	Gets the log-likelihood of the fitted model.
	MaxDegreeOfParallelism	Gets or sets the maximum degree of parallelism enabled by this instance. (Inherited from Model.)
	Method	Gets or sets the kind of logistic regression represented by this LogisticRegressionModel.
	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.)
	Parameters	Gets the collection of parameters associated with this model.
	ParameterValues	Gets the collection of parameters associated with this model.
	PredictedLogProbabilities	Gets a vector containing the model's predicted values for the dependent variable. (Inherited from ClassificationModelT.)
	PredictedProbabilities	Gets a vector containing the model's predicted values for the dependent variable. (Inherited from ClassificationModelT.)
	Predictions	Gets a vector containing the model's predicted values for the dependent variable. (Inherited from ClassificationModelT.)
	ProbabilityResiduals	Gets a matrix containing the residuals of the model. (Inherited from ClassificationModelT.)
	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.)

	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. (Inherited from ClassificationModelT.)
	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	Fits the model to the data. (Overrides ModelFitCore(ModelInput, ParallelOptions).)
	GetAkaikeInformationCriterion	Returns the Akaike information criterion (AIC) value for the model.
	GetBaseLogLikelihood	Returns the log-likelihood of the model containing only a constant term.
	GetBayesianInformationCriterion	Returns the Bayesian information criterion (BIC) value for the model.
	GetCoxAndSnellPseudoRSquared	Returns the Cox & Snell pseudo R-squared value of the model.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetInformationMatrix	Calculates the information matrix for the regression.
	GetLikelihoodRatioTest	Returns a test to verify the significance of the logistic model.
	GetLikelihoodRatioTest(LogisticRegressionModel)	Returns a test to verify the significance of the logistic model.
	GetMcFaddenPseudoRSquared	Returns the McFadden pseudo R-squared value of the model.
	GetNagelkerkePseudoRSquared	Returns the Nagelkerke pseudo R-squared value of the model.
	GetPearsonGoodnessOfFitTest	Returns the Wald test for all the parameters in the regression.
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	GetWaldTest	Returns the Wald test for all the parameters in the regression.
	GetWaldTest(Int32)	Returns the Wald test for the selected parameters in the regression.
	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. (Inherited from ClassificationModelT.)
	Predict(MatrixT, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from ClassificationModelT.)
	Predict(VectorT, ModelInputFormat)	Predicts the value of the output corresponding to the specified input. (Inherited from ClassificationModelT.)
	PredictCore(MatrixDouble, Boolean)	Predicts the value of the dependent variable based on the specified values of the features. (Overrides ClassificationModelTPredictCore(MatrixT, Boolean).)
	PredictCore(VectorDouble, Boolean)	Predicts the class based on the specified values of the features. (Overrides ClassificationModelTPredictCore(VectorT, Boolean).)
	PredictProbabilities(IDataFrame, ModelInputFormat)	Predicts the value of the output corresponding to the specified input. (Inherited from ClassificationModelT.)
	PredictProbabilities(MatrixT, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from ClassificationModelT.)
	PredictProbabilities(VectorT, ModelInputFormat)	Predicts the value of the output corresponding to the specified input. (Inherited from ClassificationModelT.)
	PredictProbabilitiesCore(MatrixDouble, MatrixDouble, Boolean)	Predicts the probabilities of each class based on the specified values of the features. (Overrides ClassificationModelTPredictProbabilitiesCore(MatrixT, MatrixDouble, Boolean).)
	PredictProbabilitiesCore(VectorDouble, VectorDouble, Boolean)	Predicts the probabilities of each class based on the specified values of the features. (Overrides ClassificationModelTPredictProbabilitiesCore(VectorT, VectorDouble, Boolean).)
	PredictProbabilitiesInto(IDataFrame, MatrixDouble, ModelInputFormat)	Predicts the value of the output corresponding to the specified input. (Inherited from ClassificationModelT.)
	PredictProbabilitiesInto(MatrixT, MatrixDouble, ModelInputFormat)	Predicts the value of the output corresponding to the specified features. (Inherited from ClassificationModelT.)
	PredictProbabilitiesInto(VectorT, VectorDouble, ModelInputFormat)	Predicts the probabilities of each class based on the specified features. (Inherited from ClassificationModelT.)
	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. (Overrides ModelSummarize(SummaryOptions).)
	ToString	Returns a string that represents the current object. (Inherited from Model.)

Use the LogisticRegressionModel class to analyze a situation where the outcome can have two or more possible values. A logistic regression model tries to express one variable, called the dependent variable, which can have only two distinct values, as a function of one or more other variables called independent variables or predictors in a specific form.

Logistic regression is a special case of a GeneralizedLinearModel with a binomial distribution and the logit link function. To perform variants of logistic regression, like probit regression, use the GeneralizedLinearModel class.

In addition to binary logistic regression, the LogisticRegressionModel can also represent multinomial logistic regression, where there may be more than two outcomes. In this case, the dependent variable must be a ICategoricalVector.

Reference