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: This returns only the set of estimator parameters currently in use
code: def _get_est_params(self):
      """This returns only the set of estimator parameters currently in use"""
      params = VW._get_est_params(self)
      params.update(dict(classes_=self.classes_, coef_=self.coef_))
      return params
  
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 6 for calling the VWClassifier obj's underlying member method code with example usage: obj._get_est_params() and returns: params