Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/17981#discussion_r117601533
--- Diff: R/pkg/R/mllib_tree.R ---
@@ -499,3 +543,199 @@ setMethod("write.ml", signature(object =
"RandomForestClassificationModel", path
function(object, path, overwrite = FALSE) {
write_internal(object, path, overwrite)
})
+
+#' Decision Tree Model for Regression and Classification
+#'
+#' \code{spark.decisionTree} fits a Decision Tree Regression model or
Classification model on
+#' a SparkDataFrame. Users can call \code{summary} to get a summary of the
fitted Decision 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#decision-tree-regression}{
+#' Decision Tree Regression} and
+#'
\href{http://spark.apache.org/docs/latest/ml-classification-regression.html#decision-tree-classifier}{
+#' Decision Tree Classification}
+#'
+#' @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).
+#' @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.
+#' @param impurity Criterion used for information gain calculation.
+#' For regression, must be "variance". For classification,
must be one of
+#' "entropy" and "gini", default is "gini".
+#' @param seed integer seed for random number generation.
+#' @param minInstancesPerNode Minimum number of instances each child must
have after split.
+#' @param minInfoGain Minimum information gain for a split to be
considered at a tree node.
+#' @param checkpointInterval Param for set checkpoint interval (>= 1) or
disable checkpoint (-1).
+#' @param maxMemoryInMB Maximum memory in MB allocated to histogram
aggregation.
+#' @param cacheNodeIds If FALSE, the algorithm will pass trees to
executors to match instances with
+#' nodes. If TRUE, the algorithm will cache node IDs
for each instance. Caching
+#' can speed up training of deeper trees. Users can
set how often should the
+#' cache be checkpointed or disable it by setting
checkpointInterval.
--- End diff --
wording can be improved a bit I guess but this matches the Scaladoc...
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