Trained SVM model as output from
The returning object consist of the following values:
call The function specifications which has been called.
lambda The regularization parameter of the penalty term which has been used.
loss The corresponding loss function value of the final solution.
iteration Number of iterations needed to evaluate the algorithm.
X The attribute matrix of
dim(X) = c(n,k).
y The vector of length
n with the actual class labels.
These labels can be numeric
[0 1] or two strings.
classes A vector of length
n with the predicted
class labels of each object, derived from q.tilde
Xtrans The attribute matrix
X after standardization and
(if specified) spline transformation.
norm.param The applied normalization parameters
splineInterval The spline knots which has been used
splineLengthDenotes the number of spline basis of
each explanatory variable in
methodThe decomposition matrices used in estimating the model.
hinge The hinge function which has been used
beta If identified, the beta parameters for the linear combination (only available for linear kernel).
q A vector of length
n with predicted values of
each object including the intercept.
nSV Number of support vectors.
1 2 3 4 5 6 7 8 9 10 11
further arguments passed to or from other methods.
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