A wrapper around mlpack::logistic_regression()
that allows passing a formula.
Usage
logistic_regression(
formula = NULL,
data = NULL,
penalty = 1e-04,
epochs = 1000,
decision_boundary = 0.5,
tolerance = 1e-10,
optimizer = c("lbfgs", "sgd"),
batch_size = 64,
learn_rate = 0.01,
x = NULL,
y = NULL
)Arguments
- formula
A formula.
- data
A data.frame.
- penalty
L2-regularization constant.
- epochs
Maximum number of iterations.
- decision_boundary
Decision boundary for prediction; if the logistic function for a point is less than the boundary, the class is taken to be 0; otherwise, the class is 1.
- tolerance
Convergence tolerance for optimizer.
- optimizer
Optimizer to use for training ("lbfgs" or "sgd").
- batch_size
Batch size for SGD.
- learn_rate
Step size for SGD optimizer.
- x
Design matrix.
- y
Response matrix.