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: Predict class labels for samples in X.
  
  Args:
      X : array_like or sparse matrix, shape (n_samples, n_features)
          Samples.
  
  Returns:
      array, shape [n_samples] : C. Predicted class label per sample.
code: def predict(self, X):
      """
      Predict class labels for samples in X.
  
      Args:
          X : array_like or sparse matrix, shape (n_samples, n_features)
              Samples.
  
      Returns:
          array, shape [n_samples] : C. Predicted class label per sample.
      """
  
      scores = self.decision_function(X)
      if len(scores.shape) == 1:
          indices = (scores > 0).astype(int)
      else:
          indices = scores.argmax(axis=1)
      return self.classes_[indices]
  
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 4 for calling the VWClassifier obj's underlying member method code with example usage: obj.predict(X) and returns: self