Github user wangmiao1981 commented on a diff in the pull request:
--- Diff: R/pkg/R/mllib.R ---
@@ -647,6 +654,195 @@ setMethod("predict", signature(object =
+#' Logistic Regression Model
+#' Fits an logistic regression model against a Spark DataFrame. It
supports "binomial": Binary logistic regression
+#' with pivoting; "multinomial": Multinomial logistic (softmax) regression
without pivoting, similar to glmnet.
+#' 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 '~', '.', ':', '+',
+#' @param regParam the regularization parameter. Default is 0.0.
+#' @param elasticNetParam the ElasticNet mixing parameter. For alpha = 0,
the penalty is an L2 penalty.
+#' For alpha = 1, it is an L1 penalty. For 0 <
alpha < 1, the penalty is a combination
+#' of L1 and L2. Default is 0.0 which is an L2
+#' @param maxIter maximum iteration number.
+#' @param tol convergence tolerance of iterations.
+#' @param fitIntercept whether to fit an intercept term. Default is TRUE.
+#' @param family the name of family which is a description of the label
distribution to be used in the model.
+#' Supported options:
+#' - "auto": Automatically select the family based on the
number of classes:
+#' If numClasses == 1 || numClasses == 2, set to
+#' Else, set to "multinomial".
+#' - "binomial": Binary logistic regression with pivoting.
+#' - "multinomial": Multinomial logistic (softmax)
regression without pivoting.
+#' Default is "auto".
+#' @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.
+#' @param threshold in binary classification, in range [0, 1]. If the
estimated probability of class label 1
+#' is > threshold, then predict 1, else 0. A high
threshold encourages the model to predict 0
+#' more often; a low threshold encourages the model to
predict 1 more often. Note: Setting this with
+#' threshold p is equivalent to setting thresholds
(Array(1-p, p)). When threshold is set, any user-set
+#' value for thresholds will be cleared. If both
threshold and thresholds are set, then they must be
+#' equivalent. Default is 0.5.
+#' @param thresholds in multiclass (or binary) classification to adjust
the probability of predicting each class.
--- End diff --
I don't quite understand this comment. There is a JIRA SPARK-11543
discussing the expected relationship, but it is not implemented yet. So, we
keep both threshold and thresholds on Scala side.
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