Usage
cv.mhingebst(x, y, balance=FALSE, K = 10, cost = NULL, family = "hinge",
learner = c("tree", "ls", "sm"), ctrl = bst_control(),
type = c("loss","error"), plot.it = TRUE, main = NULL, se = TRUE, n.cores=2, ...)
Arguments
x
a data frame containing the variables in the model.
y
vector of responses. y must be integers from 1 to C for C class problem.
balance
logical value. If TRUE, The K
parts were roughly balanced, ensuring that the classes were distributed
proportionally among each of the K parts.
K
K-fold cross-validation
cost
price to pay for false positive, 0 < cost < 1; price of false negative is 1-cost.
family
family = "hinge" for hinge loss.
learner
a character specifying the component-wise base learner to be used:
ls linear models,
sm smoothing splines,
tree regression trees.
type
for family="hinge", type="loss" is hinge risk.
plot.it
a logical value, to plot the estimated loss or error with cross validation if TRUE.
se
a logical value, to plot with standard errors.
n.cores
The number of CPU cores to use. The cross-validation loop
will attempt to send different CV folds off to different cores.
...
additional arguments.