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Last data update: 2014.03.03

R: Cross Validation

xgb.cv

R Documentation

Cross Validation

Description

The cross valudation function of xgboost

Usage

xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
  missing = NULL, prediction = FALSE, showsd = TRUE, metrics = list(),
  obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
  verbose = T, print.every.n = 1L, early.stop.round = NULL,
  maximize = 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 `eta` step size of each boosting step `max.depth` maximum depth of the tree `nthread` number of thread used in training, if not set, all threads are used See xgb.train for further details. See also demo/ for walkthrough example in R.
`data`	takes an `xgb.DMatrix` or `Matrix` as the input.
`nrounds`	the max number of iterations
`nfold`	the original dataset is randomly partitioned into `nfold` equal size subsamples.
`label`	option field, when data is `Matrix`
`missing`	Missing is only used when input is dense matrix, pick a float value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
`prediction`	A logical value indicating whether to return the prediction vector.
`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` Rooted mean square error `logloss` negative log-likelihood function `auc` Area under curve `merror` Exact matching error, used to evaluate multi-class classification
`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.
`stratified`	`boolean` whether sampling of folds should be stratified by the values of labels in `data`
`folds`	`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.
`verbose`	`boolean`, print the statistics during the process
`print.every.n`	Print every N progress messages when `verbose>0`. Default is 1 which means all messages are printed.
`early.stop.round`	If `NULL`, the early stopping function is not triggered. If set to an integer `k`, training with a validation set will stop if the performance keeps getting worse consecutively for `k` rounds.
`maximize`	If `feval` and `early.stop.round` are set, then `maximize` must be set as well. `maximize=TRUE` means the larger the evaluation score the better.
`...`	other parameters to pass to `params`.

Details

The original sample is randomly partitioned into nfold equal size subsamples.

Of the nfold subsamples, a single subsample is retained as the validation data for testing the model, and the remaining nfold - 1 subsamples are used as training data.

The cross-validation process is then repeated nrounds times, with each of the nfold subsamples used exactly once as the validation data.

All observations are used for both training and validation.

Adapted from http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29#k-fold_cross-validation

Value

If prediction = TRUE, a list with the following elements is returned:

dt a data.table with each mean and standard deviation stat for training set and test set
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 prediction = FALSE, just a data.table with each mean and standard deviation stat for training set and test set is returned.

Examples

data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
history <- xgb.cv(data = dtrain, nround=3, nthread = 2, nfold = 5, metrics=list("rmse","auc"),
                  max.depth =3, eta = 1, objective = "binary:logistic")
print(history)