[doc] Mention the integer type for some parameters. (dmlc#11266)

Python type annotation is not changed. We can use np.int32, but it's just one
implementation of int32, there are others.

Author: trivialfis

R-package/R/xgb.train.R

@@ -486,7 +486,7 @@ xgb.train <- function(params = xgb.params(), data, nrounds, evals = list(),
 #' range: \eqn{[0, \infty)}
 #'
 #' Note: should only pass one of `gamma` or `min_split_loss`. Both refer to the same parameter and there's thus no difference between one or the other.
-#' @param max_depth (for Tree Booster) (default=6)
+#' @param max_depth (for Tree Booster) (default=6, type=int32)
 #' Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit. 0 indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree. `"exact"` tree method requires non-zero value.
 #'
 #' range: \eqn{[0, \infty)}
@@ -579,9 +579,9 @@ xgb.train <- function(params = xgb.params(), data, nrounds, evals = list(),
 #' - Choices: `"depthwise"`, `"lossguide"`
 #'   - `"depthwise"`: split at nodes closest to the root.
 #'   - `"lossguide"`: split at nodes with highest loss change.
-#' @param max_leaves (for Tree Booster) (default=0)
+#' @param max_leaves (for Tree Booster) (default=0, type=int32)
 #' Maximum number of nodes to be added.  Not used by `"exact"` tree method.
-#' @param max_bin (for Tree Booster) (default=256)
+#' @param max_bin (for Tree Booster) (default=256, type=int32)
 #' - Only used if `tree_method` is set to `"hist"` or `"approx"`.
 #' - Maximum number of discrete bins to bucket continuous features.
 #' - Increasing this number improves the optimality of splits at the cost of higher computation time.

R-package/man/xgb.params.Rd

@@ -39,7 +39,7 @@ The following parameters can be set in the global scope, using :py:func:`xgboost
 ******************
 General Parameters
 ******************
-* ``booster`` [default= ``gbtree`` ]
+* ``booster`` [default= ``gbtree``]
 
   - Which booster to use. Can be ``gbtree``, ``gblinear`` or ``dart``; ``gbtree`` and ``dart`` use tree based models while ``gblinear`` uses linear functions.
 
@@ -90,7 +90,7 @@ Parameters for Tree Booster
   - Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger ``gamma`` is, the more conservative the algorithm will be. Note that a tree where no splits were made might still contain a single terminal node with a non-zero score.
   - range: [0,∞]
 
-* ``max_depth`` [default=6]
+* ``max_depth`` [default=6, type=int32]
 
   - Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit. 0 indicates no limit on depth. Beware that XGBoost aggressively consumes memory when training a deep tree. ``exact`` tree method requires non-zero value.
   - range: [0,∞]
@@ -198,11 +198,11 @@ Parameters for Tree Booster
     - ``depthwise``: split at nodes closest to the root.
     - ``lossguide``: split at nodes with highest loss change.
 
-* ``max_leaves`` [default=0]
+* ``max_leaves`` [default=0, type=int32]
 
   - Maximum number of nodes to be added.  Not used by ``exact`` tree method.
 
-* ``max_bin``, [default=256]
+* ``max_bin``, [default=256, type=int32]
 
   - Only used if ``tree_method`` is set to ``hist`` or ``approx``.
   - Maximum number of discrete bins to bucket continuous features.