Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge pull request #227 from khotilov/master

Browse Source 
add stratified cross validation for classification
This commit is contained in:
Tong He
2015-04-30 11:39:52 -07:00
parent 188d81d64a aefd234da3
commit bab7b58d94
3 changed files with 118 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
R-package/man/xgb.cv.Rd
View File

@@ -6,7 +6,8 @@
\usage{
xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
missing = NULL, prediction = FALSE, showsd = TRUE, metrics = list(),
obj = NULL, feval = NULL, verbose = T, ...)
obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
verbose = T, ...)
}
\arguments{
\item{params}{the list of parameters. Commonly used ones are:
@@ -51,18 +52,29 @@ value that represents missing value. Sometime a data use 0 or other extreme valu
}}
\item{obj}{customized objective function. Returns gradient and second order
gradient with given prediction and dtrain,}
gradient with given prediction and dtrain.}
\item{feval}{custimized evaluation function. Returns
\code{list(metric='metric-name', value='metric-value')} with given
prediction and dtrain,}
prediction and dtrain.}
\item{verbose}{\code{boolean}, print the statistics during the process.}
\item{stratified}{\code{boolean} whether sampling of folds should be stratified by the values of labels in \code{data}}
\item{folds}{\code{list} provides a possibility of using a list of pre-defined CV folds (each element must be a vector of fold's indices).
If folds are supplied, the nfold and stratified parameters would be ignored.}
\item{verbose}{\code{boolean}, print the statistics during the process}
\item{...}{other parameters to pass to \code{params}.}
}
\value{
A \code{data.table} with each mean and standard deviation stat for training set and test set.
If \code{prediction = TRUE}, a list with the following elements is returned:
\itemize{
\item \code{dt} a \code{data.table} with each mean and standard deviation stat for training set and test set
\item \code{pred} an array or matrix (for multiclass classification) with predictions for each CV-fold for the model having been trained on the data in all other folds.
}
If \code{prediction = FALSE}, just a \code{data.table} with each mean and standard deviation stat for training set and test set is returned.
}
\description{
The cross valudation function of xgboost