Github user BryanCutler commented on a diff in the pull request:

    https://github.com/apache/spark/pull/10598#discussion_r52680824
  
    --- Diff: python/pyspark/mllib/classification.py ---
    @@ -272,37 +275,42 @@ def train(cls, data, iterations=100, step=1.0, 
miniBatchFraction=1.0,
             """
             Train a logistic regression model on the given data.
     
    -        :param data:              The training data, an RDD of
    -                                  LabeledPoint.
    -        :param iterations:        The number of iterations
    -                                  (default: 100).
    -        :param step:              The step parameter used in SGD
    -                                  (default: 1.0).
    -        :param miniBatchFraction: Fraction of data to be used for each
    -                                  SGD iteration (default: 1.0).
    -        :param initialWeights:    The initial weights (default: None).
    -        :param regParam:          The regularizer parameter
    -                                  (default: 0.01).
    -        :param regType:           The type of regularizer used for
    -                                  training our model.
    -
    -                                  :Allowed values:
    -                                     - "l1" for using L1 regularization
    -                                     - "l2" for using L2 regularization
    -                                     - None for no regularization
    -
    -                                     (default: "l2")
    -
    -        :param intercept:         Boolean parameter which indicates the
    -                                  use or not of the augmented 
representation
    -                                  for training data (i.e. whether bias
    -                                  features are activated or not,
    -                                  default: False).
    -        :param validateData:      Boolean parameter which indicates if
    -                                  the algorithm should validate data
    -                                  before training. (default: True)
    -        :param convergenceTol:    A condition which decides iteration 
termination.
    -                                  (default: 0.001)
    +        :param data:
    +          The training data, an RDD of LabeledPoint.
    +        :param iterations:
    +          The number of iterations.
    +          (default: 100)
    +        :param step:
    +          The step parameter used in SGD.
    +          (default: 1.0)
    +        :param miniBatchFraction:
    +          Fraction of data to be used for each SGD iteration.
    +          (default: 1.0)
    +        :param initialWeights:
    +          The initial weights.
    +          (default: None)
    +        :param regParam:
    +          The regularizer parameter.
    +          (default: 0.01)
    +        :param regType:
    +          The type of regularizer used for training our model.
    +          :Allowed values:
    +            - "l1" for using L1 regularization
    +            - "l2" for using L2 regularization
    +            - None for no regularization
    +          (default: "l2")
    --- End diff --
    
    I believe that `:Allowed values:` would need to be preceded by a blank line 
for Sphinx to interpret it in bold like before, but I don't think that needs to 
be done here.  What @mengxr proposed above looks fine with me too.
    
    @vijaykiran , will you be able to make this fix soon?  I could take over 
and finish this last little bit if you can't.


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