class_docstr: A private abstract class representing a multiclass classification
  model. The categories are represented by int values: 0, 1, 2, etc.
class_docstr_tok: ["A","private","abstract","class","representing","a","multiclass","classification","model",".","The","categories","are","represented","by","int","values",":","0",",","1",",","2",",","etc","."]
docstr: Sets the threshold that separates positive predictions from
  negative predictions. An example with prediction score greater
  than or equal to this threshold is identified as a positive,
  and negative otherwise. It is used for binary classification
  only.
docstr_tok: ["Sets","the","threshold","that","separates","positive","predictions","from","negative","predictions",".","An","example","with","prediction","score","greater","than","or","equal","to","this","threshold","is","identified","as","a","positive",",","and","negative","otherwise",".","It","is","used","for","binary","classification","only","."]
code: def setThreshold(self, value: float) -> None:
      """
      Sets the threshold that separates positive predictions from
      negative predictions. An example with prediction score greater
      than or equal to this threshold is identified as a positive,
      and negative otherwise. It is used for binary classification
      only.
      """
      self._threshold = value
  
imports: from math import exp
  import sys
  import warnings
  from typing import Any, Iterable, Optional, Union, overload, TYPE_CHECKING
  import numpy
  from pyspark import RDD, SparkContext, since
  from pyspark.streaming.dstream import DStream
  from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py
  from pyspark.mllib.linalg import _convert_to_vector
  from pyspark.mllib.regression import LabeledPoint, LinearModel, _regression_train_wrapper, StreamingLinearAlgorithm
  from pyspark.mllib.util import Saveable, Loader, inherit_doc
  from pyspark.mllib.linalg import Vector
  from pyspark.mllib.regression import LabeledPoint
label_desc: Use image node_id 2 for calling the LinearClassificationModel obj's underlying member method code with example usage: obj.setThreshold(value) without return types