Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/16800#discussion_r100954149
--- Diff: R/pkg/R/mllib_classification.R ---
@@ -39,6 +46,116 @@ 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. Default is TRUE, same as glmnet.
--- End diff --
but my point is glmnet is linear regression whereas here we are linear svc?
isn't it not a very good reference?
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