Usage
cv.mada(x, y, balance=FALSE, K=10, nu=0.1, mstop=200, interaction.depth=1, trace=FALSE, plot.it = TRUE, se = TRUE, ...)
Arguments
x
a data matrix containing the variables in the model.
y
vector of multi class responses. y must be an interger vector 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.
nu
a small number (between 0 and 1) defining the step size or shrinkage parameter.
mstop
number of boosting iteration.
interaction.depth
used in gbm to specify the depth of trees.
trace
if TRUE, iteration results printed out.
plot.it
a logical value, to plot the cross-validation error if TRUE.
se
a logical value, to plot with 1 standard deviation curves.