LogisticRegression: (Regularized) Logistic Regression implementation

In RSSL: Implementations of Semi-Supervised Learning Approaches for Classification

(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()

RSSL documentation built on May 29, 2024, 2:38 a.m.