cv.mada: Cross-Validation for one-vs-all AdaBoost with multi-class...

Description Usage Arguments Value See Also

View source: R/mada.R

Description

Cross-validated estimation of the empirical misclassification error for boosting parameter selection.

Usage

1
2
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.

K

K-fold cross-validation

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.

...

additional arguments.

Value

object with

residmat

empirical risks in each cross-validation at boosting iterations

fraction

abscissa values at which CV curve should be computed.

cv

The CV curve at each value of fraction

cv.error

The standard error of the CV curve

...

See Also

mada


bst documentation built on May 30, 2017, 12:04 a.m.