class_docstr: Vowpal Wabbit MultiClassifier model
  Note - We are assuming the VW.predict returns probabilities, setting probabilities=False will break this assumption
class_docstr_tok: ["Vowpal","Wabbit","MultiClassifier","model","Note","-","We","are","assuming","the","VW.predict","returns","probabilities",",","setting","probabilities=False","will","break","this","assumption"]
code: def __init__(self, probabilities=True, **kwargs):
      kwargs["probabilities"] = probabilities
      super(VWMultiClassifier, self).__init__(**kwargs)
  
imports: import io
  import os
  import re
  from tempfile import NamedTemporaryFile
  from typing import Dict
  import numpy
  from scipy.sparse import csr_matrix
  from scipy.special import logit
  from sklearn.exceptions import NotFittedError
  from sklearn.base import BaseEstimator, RegressorMixin
  from sklearn.utils.extmath import log_logistic
  from sklearn.linear_model import LogisticRegression
  from sklearn.datasets import dump_svmlight_file
  from sklearn.utils import check_array, check_X_y, shuffle
  from vowpalwabbit import Workspace
label_desc: Use image node_id 1 to create a new VWMultiClassifier object from inherited base classes: VWClassifier with example: obj = VWMultiClassifier(probabilities)