Cross Validation
The cross validation function of xgboost
xgb.cv( params = list(), data, nrounds, nfold, label = NULL, missing = NA, prediction = FALSE, showsd = TRUE, metrics = list(), obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, train_folds = NULL, verbose = TRUE, print_every_n = 1L, early_stopping_rounds = NULL, maximize = NULL, callbacks = list(), ... )
params |
the list of parameters. The complete list of parameters is available in the online documentation. Below is a shorter summary:
See |
data |
takes an |
nrounds |
the max number of iterations |
nfold |
the original dataset is randomly partitioned into |
label |
vector of response values. Should be provided only when data is an R-matrix. |
missing |
is only used when input is a dense matrix. By default is set to NA, which means that NA values should be considered as 'missing' by the algorithm. Sometimes, 0 or other extreme value might be used to represent missing values. |
prediction |
A logical value indicating whether to return the test fold predictions
from each CV model. This parameter engages the |
showsd |
|
metrics, |
list of evaluation metrics to be used in cross validation, when it is not specified, the evaluation metric is chosen according to objective function. Possible options are:
|
obj |
customized objective function. Returns gradient and second order gradient with given prediction and dtrain. |
feval |
customized evaluation function. Returns
|
stratified |
a |
folds |
|
train_folds |
|
verbose |
|
print_every_n |
Print each n-th iteration evaluation messages when |
early_stopping_rounds |
If |
maximize |
If |
callbacks |
a list of callback functions to perform various task during boosting.
See |
... |
other parameters to pass to |
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.
An object of class xgb.cv.synchronous with the following elements:
call a function call.
params parameters that were passed to the xgboost library. Note that it does not
capture parameters changed by the cb.reset.parameters callback.
callbacks callback functions that were either automatically assigned or
explicitly passed.
evaluation_log evaluation history stored as a data.table with the
first column corresponding to iteration number and the rest corresponding to the
CV-based evaluation means and standard deviations for the training and test CV-sets.
It is created by the cb.evaluation.log callback.
niter number of boosting iterations.
nfeatures number of features in training data.
folds the list of CV folds' indices - either those passed through the folds
parameter or randomly generated.
best_iteration iteration number with the best evaluation metric value
(only available with early stopping).
best_ntreelimit the ntreelimit value corresponding to the best iteration,
which could further be used in predict method
(only available with early stopping).
pred CV prediction values available when prediction is set.
It is either vector or matrix (see cb.cv.predict).
models a list of the CV folds' models. It is only available with the explicit
setting of the cb.cv.predict(save_models = TRUE) callback.
data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
cv <- xgb.cv(data = dtrain, nrounds = 3, nthread = 2, nfold = 5, metrics = list("rmse","auc"),
max_depth = 3, eta = 1, objective = "binary:logistic")
print(cv)
print(cv, verbose=TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.