class_docstr: Vowpal Wabbit Classifier model for binary classification
  Use VWMultiClassifier for multiclass classification
  Note - We are assuming the VW.predict returns logits, applying link=logistic will break this assumption
class_docstr_tok: ["Vowpal","Wabbit","Classifier","model","for","binary","classification","Use","VWMultiClassifier","for","multiclass","classification","Note","-","We","are","assuming","the","VW.predict","returns","logits",",","applying","link=logistic","will","break","this","assumption"]
docstr: Fit the model according to the given training data.
  
  Args:
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          Training vector, where n_samples is the number of samples and
          n_features is the number of features.
      y : array-like of shape (n_samples,)
          Target vector relative to X.
      sample_weight : array-like of shape (n_samples,) default=None
          Array of weights that are assigned to individual samples.
          If not provided, then each sample is given unit weight.
  
  Returns:
      self
code: def fit(self, X=None, y=None, sample_weight=None):
      """
      Fit the model according to the given training data.
  
      Args:
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Training vector, where n_samples is the number of samples and
              n_features is the number of features.
          y : array-like of shape (n_samples,)
              Target vector relative to X.
          sample_weight : array-like of shape (n_samples,) default=None
              Array of weights that are assigned to individual samples.
              If not provided, then each sample is given unit weight.
  
      Returns:
          self
      """
  
      # this attribute is used to check fitted in sparsify()
      if self.coef_ is None:
          self.coef_ = csr_matrix([])
          if y is not None:
              y = check_array(
                  y, ensure_2d=False, dtype=None, accept_sparse=True
              )
              self.classes_, y = np.unique(y, return_inverse=True)
          # TODO: raise error once check_sparsify_coefficients respects binary_only flag
          # if self._more_tags.get('binary_only') and len(self.classes_) != 2:
          #    raise Exception('VWClassifier can only be used for binary classification')
      return VW.fit(self, X=X, y=y, sample_weight=sample_weight)
  
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 2 for calling the VWClassifier obj's underlying member method code with example usage: obj.fit(X, y, sample_weight) and returns: VW