[R] xgb.cv doesn't return feature names

Hi all,

Long fan of your efforts with the Xgboost algorithm/implementation. It is super fast and memory-friendly.

I found a problem when trying to see feature importance when using the `xgb.cv` function, namely that it doesn't return the features names when using the callback `cb.cv.predict(save_models = TRUE)`.

I found this trying to plot the model importance using `xgb.plot.importance`.
Does the numbers refer to the python way of counting columns (i.e., starting from 0)?

I made an MRE below:

Xgboost version: xgboost_0.90.0.2 (R package)

```R
data(iris)
library(xgboost)
library(dplyr)

iris <- filter(iris, Species != 'setosa')
features <- as.matrix(iris[, !grepl('Species', colnames(iris))])
label <- ifelse(iris$Species == 'virginica', 1, 0)

model <- xgboost::xgb.cv(
data = features
, label = label
, nfold = 5
, nrounds = 25
, metrics = list("auc")
, stratified = TRUE
, verbose = TRUE
, callbacks = list(cb.cv.predict(save_models = TRUE))

, params = list(
eta = 0.1
, max_depth = 10
, objective = "binary:logistic"
, colsample_bytree = 0.5
, subsample = 0.5
, nthread = 2
, seed = 1
)
)

importance <- xgb.importance(model = model$models[[1]])
xgboost::xgb.plot.importance(importance)

```