# xgb.cv

From xgboost v0.3-2
by Tong He

##### Cross Validation

The cross valudation function of xgboost

##### Usage

```
xgb.cv(params = list(), data, nrounds, nfold, label = NULL, showsd = TRUE,
metrics = list(), obj = NULL, feval = NULL, ...)
```

##### Arguments

- params
- the list of parameters. Commonly used ones are:
`objective`

objective function, common ones are`reg:linear`

linear regression`binary:logistic`

logistic regression for classification

- data
- takes an
`xgb.DMatrix`

as the input. - nrounds
- the max number of iterations
- nfold
- number of folds used
- label
- option field, when data is Matrix
- showsd
- boolean, whether show standard deviation of cross validation
- metrics,
- list of evaluation metrics to be used in corss validation,
when it is not specified, the evaluation metric is chosen according to objective function.
Possible options are:
`error`

binary classification error rate`rmse`

- obj
- customized objective function. Returns gradient and second order gradient with given prediction and dtrain,
- feval
- custimized evaluation function. Returns
`list(metric='metric-name', value='metric-value')`

with given prediction and dtrain, - ...
- other parameters to pass to
`params`

.

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##### Details

This is the cross validation function for xgboost

Parallelization is automatically enabled if OpenMP is present.
Number of threads can also be manually specified via "nthread" parameter.

This function only accepts an `xgb.DMatrix`

object as the input.

##### Examples

```
data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
"max.depth"=3, "eta"=1, "objective"="binary:logistic")
```

* Documentation reproduced from package xgboost, version 0.3-2,
License: Apache License (== 2.0) | file LICENSE
*
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