LeastSquaresClassifier: Least Squares Classifier

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

Least Squares Classifier

Description

Classifier that minimizes the quadratic loss or, equivalently, least squares regression applied to a numeric encoding of the class labels as target. Note this method minimizes quadratic loss, not the truncated quadratic loss. Optionally, L2 regularization can be applied by setting the lambda parameter.

Usage

LeastSquaresClassifier(X, y, lambda = 0, intercept = TRUE,
  x_center = FALSE, scale = FALSE, method = "inverse", y_scale = FALSE)

Arguments

X	matrix; Design matrix for labeled data
y	factor or integer vector; Label vector
lambda	Regularization parameter of the l2 penalty
intercept	TRUE if an intercept should be added to the model
x_center	TRUE, whether the dependent variables (features) should be centered
scale	If TRUE, apply a z-transform to the design matrix X before running the regression
method	Method to use for fitting. One of c("inverse","Normal","QR","BFGS")
y_scale	If True scale the target vector

Value

S4 object of class LeastSquaresClassifier with the following slots:

theta	weight vector
classnames	the names of the classes
modelform	formula object of the model used in regression
scaling	a scaling object containing the parameters of the z-transforms applied to the data

See Also

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

jkrijthe/RSSL documentation built on Jan. 13, 2024, 1:56 a.m.