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 = 
                 predict_internal(object, newData)
    +#' 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 '~', '.', ':', '+', 
and '-'.
    +#' @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.
    +#'                   Array must have length equal to the number of 
classes, with values > 0, excepting that at most one
    --- End diff --
    Modified to `c(p, 1-p)`

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