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

cv.mada {bst}

R Documentation

Cross-Validation for one-vs-all AdaBoost with multi-class problem

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

cv.mada(x, y, balance=FALSE, K=10, nu=0.1, mstop=200, interaction.depth=1, 
trace=FALSE, plot.it = TRUE, se = TRUE, ...)

`x`	a data matrix containing the variables in the model.
`y`	vector of multi class responses. `y` must be an integer 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.

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

...