Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15746#discussion_r86564876
--- Diff: R/pkg/R/mllib.R ---
@@ -1863,5 +1884,198 @@ print.summary.RandomForestRegressionModel <-
function(x, ...) {
#' @export
#' @note print.summary.RandomForestClassificationModel since 2.1.0
print.summary.RandomForestClassificationModel <- function(x, ...) {
- print.summary.randomForest(x)
+ print.summary.treeEnsemble(x)
+}
+
+#' Gradient Boosted Tree Model for Regression and Classification
+#'
+#' \code{spark.gbt} fits a Gradient Boosted Tree Regression model or
Classification model on a
+#' SparkDataFrame. Users can call \code{summary} to get a summary of the
fitted
+#' Gradient Boosted Tree model, \code{predict} to make predictions on new
data, and
+#' \code{write.ml}/\code{read.ml} to save/load fitted models.
+#' For more details, see
+#'
\href{http://spark.apache.org/docs/latest/ml-classification-regression.html}{GBT}
+#'
+#' @param data a SparkDataFrame for training.
+#' @param formula a symbolic description of the model to be fitted.
Currently only a few formula
+#' operators are supported, including '~', ':', '+', and
'-'.
+#' @param type type of model, one of "regression" or "classification", to
fit
+#' @param maxDepth Maximum depth of the tree (>= 0). (default = 5)
+#' @param maxBins Maximum number of bins used for discretizing continuous
features and for choosing
+#' how to split on features at each node. More bins give
higher granularity. Must be
+#' >= 2 and >= number of categories in any categorical
feature. (default = 32)
+#' @param maxIter Param for maximum number of iterations (>= 0).
+#' @param stepSize Param for Step size to be used for each iteration of
optimization.
+#' @param lossType Loss function which GBT tries to minimize.
+#' For classification, must be "logistic". For regression,
must be one of
+#' "squared" (L2) and "absolute" (L1). (default =
"squared")
+#' @param seed integer seed for random number generation.
+#' @param subsamplingRate Fraction of the training data used for learning
each decision tree, in
+#' range (0, 1]. (default = 1.0)
+#' @param minInstancesPerNode Minimum number of instances each child must
have after split. If a
+#' split causes the left or right child to have
fewer than
+#' minInstancesPerNode, the split will be
discarded as invalid. Should be
+#' >= 1.
+#' @param minInfoGain Minimum information gain for a split to be
considered at a tree node.
--- End diff --
```(default = 0.0)```
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