Logistic Loss Classifier
Find the linear classifier which minimizing the logistic loss on the training set, optionally using L2 regularization.
LogisticLossClassifier(X, y, lambda = 0, intercept = TRUE, scale = FALSE, init = NA, x_center = FALSE, ...)
X |
Design matrix, intercept term is added within the function |
y |
Vector with class assignments |
lambda |
Regularization parameter used for l2 regularization |
intercept |
TRUE if an intercept should be added to the model |
scale |
If TRUE, apply a z-transform to all observations in X and X_u before running the regression |
init |
Starting parameter vector for gradient descent |
x_center |
logical; Whether the feature vectors should be centered |
... |
additional arguments |
S4 object with the following slots
w |
the weight vector of the linear classifier |
classnames |
vector with names of the classes |
Other RSSL classifiers:
EMLeastSquaresClassifier,
EMLinearDiscriminantClassifier,
GRFClassifier,
ICLeastSquaresClassifier,
ICLinearDiscriminantClassifier,
KernelLeastSquaresClassifier,
LaplacianKernelLeastSquaresClassifier(),
LaplacianSVM,
LeastSquaresClassifier,
LinearDiscriminantClassifier,
LinearSVM,
LinearTSVM(),
LogisticRegression,
MCLinearDiscriminantClassifier,
MCNearestMeanClassifier,
MCPLDA,
MajorityClassClassifier,
NearestMeanClassifier,
QuadraticDiscriminantClassifier,
S4VM,
SVM,
SelfLearning,
TSVM,
USMLeastSquaresClassifier,
WellSVM,
svmlin()
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