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"]
docstr: Predict confidence scores for samples.
  The confidence score for a sample is the signed distance of that
  sample to the hyperplane.
  
  Args:
      X : array_like or sparse matrix, shape (n_samples, n_features)
          Samples.
  
  Returns:
      array, shape=(n_samples, n_classes): Confidence scores per (sample, class) combination.
code: def decision_function(self, X):
      """
      Predict confidence scores for samples.
      The confidence score for a sample is the signed distance of that
      sample to the hyperplane.
  
      Args:
          X : array_like or sparse matrix, shape (n_samples, n_features)
              Samples.
  
      Returns:
          array, shape=(n_samples, n_classes): Confidence scores per (sample, class) combination.
      """
  
      logits = logit(VW.predict(self, X=X))
      if logits.shape[1] == 2:
          logits = logits[:, 1]
      return logits
  
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 3 for calling the VWMultiClassifier obj's underlying member method code with example usage: obj.decision_function(X) and returns: logits