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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