LogisticRegression Methods

The likelihood ratio test of the overall model, also called the model chi-square test.

The likelihood ratio test of the overall model, also called the model chi-square test.

Creates a new GeneralizedLinearRegression that is a copy of the current instance.

Computes the model output for the given input vector.

Computes the model output for each of the given input vectors.

Computes a class-label decision for a given input.

Computes class-label decisions for a given set of input vectors.

Computes class-label decisions for the given input.

Computes a class-label decision for a given input.

Determines whether the specified object is equal to the current object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Constructs a new LogisticRegression from an array of weights (linear coefficients). The first weight is interpreted as the intercept value.

Constructs a new LogisticRegression from an array of weights (linear coefficients). The first weight is interpreted as the intercept value.

Gets a coefficient value, where 0 is the intercept term and the other coefficients are indexed starting at 1.

Gets the 95% confidence interval for the Odds Ratio for a given coefficient.

Gets the confidence interval for an input point.

Gets the degrees of freedom when fitting the regression.

Gets the Deviance for the model.

Gets the Deviance for the model.

Serves as the default hash function.

Gets the Log-Likelihood for the model.

Gets the Log-Likelihood for the model.

Gets the Log-Likelihood Ratio between two models.

Gets the Log-Likelihood Ratio between two models.

Gets the Odds Ratio for a given coefficient.

Gets the prediction interval for an input point.

Gets the standard error of the prediction for a particular input vector.

Gets the standard error of the fit for a particular input vector.

Gets the standard error for each coefficient.

Gets the Type of the current instance.

Gets the Wald Test for a given coefficient.

Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class.

Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class.

Predicts a class label vector for the given input vector, returning the log-likelihood that the input vector belongs to its predicted class.

Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class.

Predicts a class label vector for the given input vectors, returning the log-likelihood that the input vector belongs to its predicted class.

Predicts a class label for each input vector, returning the log-likelihood that each vector belongs to its predicted class.

Computes the log-likelihood that the given input vector belongs to each of the possible classes.

Computes the log-likelihoods that the given input vectors belongs to each of the possible classes.

Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class.

Computes the log-likelihood that the given input vector belongs to each of the possible classes.

Computes the log-likelihoods that the given input vectors belongs to each of the possible classes.

Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class.

Predicts a class label vector for the given input vector, returning the log-likelihoods of the input vector belonging to each possible class.

Predicts a class label vector for each input vector, returning the log-likelihoods of the input vector belonging to each possible class.

Creates a shallow copy of the current Object.

Computes the probabilities that the given input vector belongs to each of the possible classes.

Computes the probabilities that the given input vectors belongs to each of the possible classes.

Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class.

Computes the probabilities that the given input vector belongs to each of the possible classes.

Computes the probabilities that the given input vectors belongs to each of the possible classes.

Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class.

Predicts a class label vector for the given input vector, returning the probabilities of the input vector belonging to each possible class.

Predicts a class label vector for each input vector, returning the probabilities of the input vector belonging to each possible class.

Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.

Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.

Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.

Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted class.

Predicts a class label for the given input vector, returning the probability that the input vector belongs to its predicted class.

Predicts a class label for each input vector, returning the probability that each vector belongs to its predicted class.

Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier).

Computes a numerical score measuring the association between the given input vector and its most strongly associated class (as predicted by the classifier).

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.

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.

Computes a numerical score measuring the association between the given input vector and each class.

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.

Computes a numerical score measuring the association between the given input vector and each class.

Computes a numerical score measuring the association between the given input vector and each class.

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.

Computes a numerical score measuring the association between the given input vector and each class.

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.

Computes a numerical score measuring the association between the given input vector and each class.

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.

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.

Sets a coefficient value, where 0 is the intercept term and the other coefficients are indexed starting at 1.

Views this instance as a multi-class generative classifier, giving access to more advanced methods, such as the prediction of integer labels.

Views this instance as a multi-label generative classifier, giving access to more advanced methods, such as the prediction of one-hot vectors.

Returns a string that represents the current object.

Applies the transformation to an input, producing an associated output.

Applies the transformation to a set of input vectors, producing an associated set of output vectors.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to an input, producing an associated output.

Applies the transformation to a set of input vectors, producing an associated set of output vectors.

Applies the transformation to a set of input vectors, producing an associated set of output vectors.

Applies the transformation to a set of input vectors, producing an associated set of output vectors.

Applies the transformation to an input, producing an associated output.

Checks whether an object implements a method with the given name.

Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.

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.

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.