[R] Remove parameters and attributes related to ntree and rebase iterationrange (#9935)

R-package/man/cb.cv.predict.Rd

@@ -35,8 +35,6 @@ Callback function expects the following values to be set in its calling frame:
 \code{data},
 \code{end_iteration},
 \code{params},
-\code{num_parallel_tree},
-\code{num_class}.
 }
 \seealso{
 \code{\link{callbacks}}

R-package/man/cb.early.stop.Rd

@@ -55,7 +55,6 @@ Callback function expects the following values to be set in its calling frame:
 \code{iteration},
 \code{begin_iteration},
 \code{end_iteration},
-\code{num_parallel_tree}.
 }
 \seealso{
 \code{\link{callbacks}},

R-package/man/predict.xgb.Booster.Rd

@@ -9,7 +9,6 @@
   newdata,
   missing = NA,
   outputmargin = FALSE,
-  ntreelimit = NULL,
   predleaf = FALSE,
   predcontrib = FALSE,
   approxcontrib = FALSE,
@@ -36,8 +35,6 @@ missing values in data (e.g., 0 or some other extreme value).}
 sum of predictions from boosting iterations' results. E.g., setting \code{outputmargin=TRUE} for
 logistic regression would return log-odds instead of probabilities.}
 
-\item{ntreelimit}{Deprecated, use \code{iterationrange} instead.}
-
 \item{predleaf}{Whether to predict pre-tree leaf indices.}
 
 \item{predcontrib}{Whether to return feature contributions to individual predictions (see Details).}
@@ -53,11 +50,18 @@ or \code{predinteraction} is \code{TRUE}.}
 \item{training}{Whether the predictions are used for training. For dart booster,
 training predicting will perform dropout.}
 
-\item{iterationrange}{Specifies which trees are used in prediction. For
-example, take a random forest with 100 rounds.
-With \code{iterationrange=c(1, 21)}, only the trees built during \verb{[1, 21)} (half open set)
-rounds are used in this prediction. The index is 1-based just like an R vector. When set
-to \code{c(1, 1)}, XGBoost will use all trees.}
+\item{iterationrange}{Sequence of rounds/iterations from the model to use for prediction, specified by passing
+a two-dimensional vector with the start and end numbers in the sequence (same format as R's \code{seq} - i.e.
+base-1 indexing, and inclusive of both ends).
+
+\if{html}{\out{<div class="sourceCode">}}\preformatted{   For example, passing `c(1,20)` will predict using the first twenty iterations, while passing `c(1,1)` will
+   predict using only the first one.
+
+   If passing `NULL`, will either stop at the best iteration if the model used early stopping, or use all
+   of the iterations (rounds) otherwise.
+
+   If passing "all", will use all of the rounds regardless of whether the model had early stopping or not.
+}\if{html}{\out{</div>}}}
 
 \item{strict_shape}{Default is \code{FALSE}. When set to \code{TRUE}, the output
 type and shape of predictions are invariant to the model type.}
@@ -145,7 +149,7 @@ bst <- xgb.train(
 # use all trees by default
 pred <- predict(bst, test$data)
 # use only the 1st tree
-pred1 <- predict(bst, test$data, iterationrange = c(1, 2))
+pred1 <- predict(bst, test$data, iterationrange = c(1, 1))
 
 # Predicting tree leafs:
 # the result is an nsamples X ntrees matrix
@@ -216,7 +220,7 @@ str(pred)
 all.equal(pred, pred_labels)
 # prediction from using only 5 iterations should result
 # in the same error as seen in iteration 5:
-pred5 <- predict(bst, as.matrix(iris[, -5]), iterationrange = c(1, 6))
+pred5 <- predict(bst, as.matrix(iris[, -5]), iterationrange = c(1, 5))
 sum(pred5 != lb) / length(lb)
 
 }

R-package/man/xgb.cv.Rd

@@ -135,7 +135,6 @@ It is created by the \code{\link{cb.evaluation.log}} callback.
 parameter or randomly generated.
 \item \code{best_iteration} iteration number with the best evaluation metric value
 (only available with early stopping).
-\item \code{best_ntreelimit} and the \code{ntreelimit} Deprecated attributes, use \code{best_iteration} instead.
 \item \code{pred} CV prediction values available when \code{prediction} is set.
 It is either vector or matrix (see \code{\link{cb.cv.predict}}).
 \item \code{models} a list of the CV folds' models. It is only available with the explicit