Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/16800#discussion_r101121442
--- Diff: R/pkg/R/mllib_classification.R ---
@@ -39,6 +46,131 @@ setClass("MultilayerPerceptronClassificationModel",
representation(jobj = "jobj"
#' @note NaiveBayesModel since 2.0.0
setClass("NaiveBayesModel", representation(jobj = "jobj"))
+#' linear SVM Model
+#'
+#' Fits an linear SVM model against a SparkDataFrame. It is a binary
classifier, similar to svm in glmnet package
+#' Users can print, make predictions on the produced model and save the
model to the input path.
+#'
+#' @param data 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 regParam The regularization parameter.
+#' @param maxIter Maximum iteration number.
+#' @param tol Convergence tolerance of iterations.
+#' @param standardization Whether to standardize the training features
before fitting the model. The coefficients
+#' of models will be always returned on the
original scale, so it will be transparent for
+#' users. Note that with/without standardization,
the models should be always converged
+#' to the same solution when no regularization is
applied.
+#' @param threshold The threshold in binary classification, in range [0,
1].
+#' @param weightCol The weight column name.
+#' @param aggregationDepth The depth for treeAggregate (greater than or
equal to 2). If the dimensions of features
+#' or the number of partitions are large, this
param could be adjusted to a larger size.
+#' This is an expert parameter. Default value
should be good for most cases.
+#' @param ... additional arguments passed to the method.
+#' @return \code{spark.svmLinear} returns a fitted linear SVM model.
+#' @rdname spark.svmLinear
+#' @aliases spark.svmLinear,SparkDataFrame,formula-method
+#' @name spark.svmLinear
+#' @export
+#' @examples
+#' \dontrun{
+#' sparkR.session()
+#' df <- createDataFrame(iris)
+#' training <- df[df$Species %in% c("versicolor", "virginica"), ]
+#' model <- spark.svmLinear(training, Species ~ ., regParam = 0.5)
+#' summary <- summary(model)
+#'
+#' # fitted values on training data
+#' fitted <- predict(model, training)
+#'
+#' # save fitted model to input path
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#'
+#' # can also read back the saved model and predict
+#' # Note that summary deos not work on loaded model
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#' }
+#' @note spark.svmLinear since 2.2.0
+setMethod("spark.svmLinear", signature(data = "SparkDataFrame", formula =
"formula"),
+ function(data, formula, regParam = 0.0, maxIter = 100, tol =
1E-6, standardization = TRUE,
+ threshold = 0.5, weightCol = NULL, aggregationDepth =
2) {
--- End diff --
shouldn't we change threashold = 0.0 to match scala as discussed here
https://github.com/apache/spark/pull/16800#discussion_r100710324
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