R: Cross-Validation for Nonconvex Multi-class Loss Boosting

cv.rmbst {bst}

R Documentation

Cross-Validation for Nonconvex Multi-class Loss Boosting

Description

Cross-validated estimation of the empirical multi-class loss, can be used for tuning parameter selection.

Usage

cv.rmbst(x, y, balance=FALSE, K = 10, cost = NULL, rfamily = c("thinge", "closs"), 
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`.
`rfamily`	`rfamily` = "thinge" for truncated multi-class hinge loss.

Implementing the negative gradient corresponding to the loss function to be minimized.

`learner`	a character specifying the component-wise base learner to be used: `ls` linear models, `sm` smoothing splines, `tree` regression trees.
`ctrl`	an object of class `bst_control`.
`type`	loss value or misclassification error.
`plot.it`	a logical value, to plot the estimated loss or error with cross validation if `TRUE`.
`main`	title of plot
`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.

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

...

Author(s)