Least Squares Classifier
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.
LeastSquaresClassifier(X, y, lambda = 0, intercept = TRUE, x_center = FALSE, scale = FALSE, method = "inverse", y_scale = FALSE)
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 |
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 |
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()
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