| LogisticRegression {RSSL} | R Documentation |
(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()