L2-regularized support vector machine

A wrapper around mlpack::linear_svm() that allows passing a formula.

Usage

linear_svm(
  formula = NULL,
  data = NULL,
  margin = 1,
  penalty = 1e-04,
  epochs = 1000,
  no_intercept = FALSE,
  tolerance = 1e-10,
  optimizer = c("lbfgs", "psgd"),
  stop_iter = 50,
  learn_rate = 0.01,
  shuffle = FALSE,
  seed = 0,
  x = NULL,
  y = NULL
)

Arguments

formula

A formula.

data

A data.frame.

margin

Margin of difference between correct class and other classes.

penalty

L2-regularization constant.

epochs

Maximum iterations for optimizer (0 indicates no limit). This argument is passed as max_iterations, not as epochs for mlpack::linear_svm().

no_intercept

Logical; passed to mlpack::linear_svm().

tolerance

Convergence tolerance for optimizer.

optimizer

Optimizer to use for training ("lbfgs" or "psgd").

stop_iter

Maximum number of full epochs over dataset for parallel SGD.

learn_rate

Step size for parallel SGD optimizer. in which data points are visited for parallel SGD.

shuffle

Logical; if true, doesn't shuffle the order.

seed

Random seed. If 0, std::time(NULL) is used internally.

x

Design matrix.

y

Response matrix.

Value

An object of class baritsu_svm.

See also

mlpack::linear_svm() predict.baritsu_svm()