Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/13690#discussion_r82512787
--- Diff: R/pkg/R/mllib.R ---
@@ -1427,3 +1447,185 @@ print.summary.KSTest <- function(x, ...) {
cat(summaryStr, "\n")
invisible(x)
}
+
+#' 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{https://en.wikipedia.org/wiki/Decision_tree_learning}{Decision Tree}
+#'
+#' @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 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. (default = 32)
+#' @param ... additional arguments passed to the method.
+#' @aliases spark.decisionTree,SparkDataFrame,formula-method
+#' @return \code{spark.decisionTree} returns a fitted Decision Tree model.
+#' @rdname spark.decisionTree
+#' @name spark.decisionTree
+#' @export
+#' @examples
+#' \dontrun{
+#' df <- createDataFrame(longley)
+#'
+#' # fit a Decision Tree Regression Model
+#' model <- spark.decisionTree(data, Employed ~ ., type = "regression",
maxDepth = 5, maxBins = 16)
+#'
+#' # get the summary of the model
+#' summary(model)
+#'
+#' # make predictions
+#' predictions <- predict(model, df)
+#'
+#' # save and load the model
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#' }
+#' @note spark.decisionTree since 2.1.0
+setMethod("spark.decisionTree", signature(data = "SparkDataFrame", formula
= "formula"),
+ function(data, formula, type = c("regression", "classification"),
+ maxDepth = 5, maxBins = 32 ) {
+ type <- match.arg(type)
+ formula <- paste(deparse(formula), collapse = "")
+ switch(type,
+ regression = {
+ jobj <-
callJStatic("org.apache.spark.ml.r.DecisionTreeRegressorWrapper",
+ "fit", data@sdf, formula,
as.integer(maxDepth),
+ as.integer(maxBins))
+ new("DecisionTreeRegressionModel", jobj = jobj)
+ },
+ classification = {
+ jobj <-
callJStatic("org.apache.spark.ml.r.DecisionTreeClassifierWrapper",
+ "fit", data@sdf, formula,
as.integer(maxDepth),
+ as.integer(maxBins))
+ new("DecisionTreeClassificationModel", jobj = jobj)
+ }
+ )
+ })
+
+# Makes predictions from a Decision Tree Regression model or
+# a model produced by spark.decisionTree()
--- End diff --
Isn't the `Decision Tree Regression model` produced by
`spark.decisionTree()`? could you clarify?
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