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RSSL (version 0.9.3)

LogisticRegression: (Regularized) Logistic Regression implementation

Description

Implementation of Logistic Regression that is useful for comparisons with semi-supervised logistic regression implementations, such as EntropyRegularizedLogisticRegression.

Usage

LogisticRegression(X, y, lambda = 0, intercept = TRUE, scale = FALSE,
  init = NA, x_center = FALSE)

Arguments

X

matrix; Design matrix for labeled data

y

factor or integer vector; Label vector

lambda

numeric; L2 regularization parameter

intercept

logical; Whether an intercept should be included

scale

logical; Should the features be normalized? (default: FALSE)

init

numeric; Initialization of parameters for the optimization

x_center

logical; Should the features be centered?

See Also

Other RSSL classifiers: EMLeastSquaresClassifier, EMLinearDiscriminantClassifier, GRFClassifier, ICLeastSquaresClassifier, ICLinearDiscriminantClassifier, KernelLeastSquaresClassifier, LaplacianKernelLeastSquaresClassifier(), LaplacianSVM, LeastSquaresClassifier, LinearDiscriminantClassifier, LinearSVM, LinearTSVM(), LogisticLossClassifier, MCLinearDiscriminantClassifier, MCNearestMeanClassifier, MCPLDA, MajorityClassClassifier, NearestMeanClassifier, QuadraticDiscriminantClassifier, S4VM, SVM, SelfLearning, TSVM, USMLeastSquaresClassifier, WellSVM, svmlin()