﻿ GeneralizedLinearRegression Methods # GeneralizedLinearRegression Methods

The GeneralizedLinearRegression type exposes the following members. Methods
NameDescription ChiSquare(Double, Double)
The likelihood ratio test of the overall model, also called the model chi-square test. ChiSquare(Double, Double, Double)
The likelihood ratio test of the overall model, also called the model chi-square test. Clone
Creates a new GeneralizedLinearRegression that is a copy of the current instance. Compute(Double) Obsolete.
Computes the model output for the given input vector. Compute(Double) Obsolete.
Computes the model output for each of the given input vectors. Decide(TInput)
Computes a class-label decision for a given input.
(Inherited from BinaryScoreClassifierBaseTInput.) Decide(TInput)
Computes class-label decisions for a given set of input vectors.
(Inherited from ClassifierBaseTInput, TClasses.) Decide(TInput, Boolean)
Computes class-label decisions for the given input.
(Inherited from BinaryClassifierBaseTInput.) Decide(TInput, Boolean)
Computes a class-label decision for a given input.
(Inherited from BinaryScoreClassifierBaseTInput.) 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.)  FromLogisticRegression Obsolete.
Creates a GeneralizedLinearRegression from a LogisticRegression object. GetCoefficient
Gets a coefficient value, where 0 is the intercept term and the other coefficients are indexed starting at 1. GetConfidenceInterval
Gets the confidence interval for an input point. GetDegreesOfFreedom
Gets the degrees of freedom when fitting the regression. GetDeviance(Double, Double)
Gets the Deviance for the model. GetDeviance(Double, Double, Double)
Gets the Deviance for the model. GetHashCode
Serves as the default hash function.
(Inherited from Object.) GetLogLikelihood(Double, Double)
Gets the Log-Likelihood for the model. GetLogLikelihood(Double, Double, Double)
Gets the Log-Likelihood for the model. GetLogLikelihoodRatio(Double, Double, GeneralizedLinearRegression)
Gets the Log-Likelihood Ratio between two models. GetLogLikelihoodRatio(Double, Double, Double, GeneralizedLinearRegression)
Gets the Log-Likelihood Ratio between two models. GetPredictionInterval
Gets the prediction interval for an input point. GetPredictionStandardError
Gets the standard error of the prediction for a particular input vector. GetStandardError
Gets the standard error of the fit for a particular input vector. GetStandardErrors
Gets the standard error for each coefficient. GetType
Gets the Type of the current instance.
(Inherited from Object.) GetWaldTest
Gets the Wald Test for a given coefficient. LogLikelihood(TInput)
Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihood(TInput)
Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihood(TInput, Boolean)
Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihood(TInput, Int32)
Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihood(Double, Double)
Predicts a class label vector for the given input vectors, returning the log-likelihood that the input vector belongs to its predicted class.
(Overrides BinaryLikelihoodClassifierBaseTInputLogLikelihood(TInput, Double).) LogLikelihood(TInput, Boolean, Double)
Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihoods(TInput)
Computes the log-likelihood that the given input vector belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihoods(TInput)
Computes the log-likelihoods that the given input vectors belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihoods(TInput, Boolean)
Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihoods(TInput, Double)
Computes the log-likelihood that the given input vector belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihoods(TInput, Double)
Computes the log-likelihoods that the given input vectors belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihoods(TInput, Int32)
Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihoods(TInput, Boolean, Double)
Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) LogLikelihoods(TInput, Boolean, Double)
Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) MemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.) Probabilities(TInput)
Computes the probabilities that the given input vector belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probabilities(TInput)
Computes the probabilities that the given input vectors belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probabilities(TInput, Boolean)
Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probabilities(TInput, Double)
Computes the probabilities that the given input vector belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probabilities(TInput, Double)
Computes the probabilities that the given input vectors belongs to each of the possible classes.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probabilities(TInput, Int32)
Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probabilities(TInput, Boolean, Double)
Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probabilities(TInput, Boolean, Double)
Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probability(TInput)
Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probability(TInput)
Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probability(TInput, Boolean)
Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probability(TInput, Int32)
Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Probability(Double, Double)
Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.
(Overrides BinaryLikelihoodClassifierBaseTInputProbability(TInput, Double).) Probability(TInput, Boolean, Double)
Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted class.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Score(TInput)
Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier).
(Inherited from BinaryScoreClassifierBaseTInput.) Score(TInput)
Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier).
(Inherited from BinaryScoreClassifierBaseTInput.) Score(TInput, Boolean)
Predicts a class label for the input vector, returning a numerical score measuring the strength of association of the input vector to its most strongly related class.
(Inherited from BinaryScoreClassifierBaseTInput.) Score(TInput, Boolean)
Predicts a class label for each input vector, returning a numerical score measuring the strength of association of the input vector to the most strongly related class.
(Inherited from BinaryScoreClassifierBaseTInput.) Score(Double, Double)
Computes a numerical score measuring the association between the given input vector and each class.
(Overrides BinaryLikelihoodClassifierBaseTInputScore(TInput, Double).) Score(TInput, Boolean, Double)
Predicts a class label for each input vector, returning a numerical score measuring the strength of association of the input vector to the most strongly related class.
(Inherited from BinaryScoreClassifierBaseTInput.) Scores(TInput)
Computes a numerical score measuring the association between the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.) Scores(TInput)
Computes a numerical score measuring the association between the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.) Scores(TInput, Boolean)
Predicts a class label vector for the given input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.) Scores(TInput, Double)
Computes a numerical score measuring the association between the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.) Scores(TInput, Boolean)
Predicts a class label vector for each input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.) Scores(TInput, Double)
Computes a numerical score measuring the association between the given input vector and each class.
(Inherited from BinaryScoreClassifierBaseTInput.) Scores(TInput, Boolean, Double)
Predicts a class label vector for the given input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.) Scores(TInput, Boolean, Double)
Predicts a class label vector for each input vector, returning a numerical score measuring the strength of association of the input vector to each of the possible classes.
(Inherited from BinaryScoreClassifierBaseTInput.) SetCoefficient
Sets a coefficient value, where 0 is the intercept term and the other coefficients are indexed starting at 1. ToMulticlass
Views this instance as a multi-class generative classifier, giving access to more advanced methods, such as the prediction of integer labels.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) ToMultilabel
Views this instance as a multi-label generative classifier, giving access to more advanced methods, such as the prediction of one-hot vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) ToString
Returns a string that represents the current object.
(Inherited from Object.) Transform(TInput)
Applies the transformation to an input, producing an associated output.
(Inherited from ClassifierBaseTInput, TClasses.) Transform(TInput)
Applies the transformation to a set of input vectors, producing an associated set of output vectors.
(Inherited from TransformBaseTInput, TOutput.) Transform(TInput, Boolean)
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.) Transform(TInput, Int32)
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.) Transform(TInput, Boolean)
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.) Transform(TInput, Int32)
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.) Transform(TInput, Int32)
Applies the transformation to an input, producing an associated output.
(Inherited from BinaryClassifierBaseTInput.) Transform(TInput, Double)
Applies the transformation to a set of input vectors, producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Transform(TInput, Double)
Applies the transformation to a set of input vectors, producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Transform(TInput, Double)
Applies the transformation to a set of input vectors, producing an associated set of output vectors.
(Inherited from BinaryLikelihoodClassifierBaseTInput.) Transform(TInput, TClasses)
Applies the transformation to an input, producing an associated output.
(Inherited from ClassifierBaseTInput, TClasses.)
Top Extension Methods
NameDescription 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.) ToTOverloaded.
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.)
Top See Also