Argument of: kernlab::ksvm

cost of constraints violation (default: 1) this is the ‘C’-constant of the regularization term in the Lagrange formulation.

Argument of: kernlab::ksvm

parameter needed for nu-svc, one-svc, and nu-svr. The nu parameter sets the upper bound on the training error and the lower bound on the fraction of data points to become Support Vectors (default: 0.2).

Argument of: kernlab::ksvm

epsilon in the insensitive-loss function used for eps-svr, nu-svr and eps-bsvm (default: 0.1)

Argument of: kernlab::ksvm

a named vector of weights for the different classes, used for asymmetric class sizes. Not all factor levels have to be supplied (default weight: 1). All components have to be named.

Argument of: kernlab::ksvm

cache memory in MB (default 40)

Argument of: kernlab::ksvm

tolerance of termination criterion (default: 0.001)

Argument of: kernlab::ksvm

option whether to use the shrinking-heuristics (default: TRUE)

Argument of: kernlab::ksvm

indicates whether the fitted values should be computed and included in the model or not (default: TRUE)

Error : $ operator is invalid for atomic vectors

Integral over the graph that results from computing fpr and tpr for many different thresholds.

$ operator is invalid for atomic vectors