class_docstr: Abstract base class for naive Bayes on discrete/categorical data
  
  Any estimator based on this class should provide:
  
  __init__
  _joint_log_likelihood(X) as per BaseNB
class_docstr_tok: ["Abstract","base","class","for","naive","Bayes","on","discrete\/categorical","data","Any","estimator","based","on","this","class","should","provide",":","__init__","_joint_log_likelihood","(","X",")","as","per","BaseNB"]
code: def _get_coef(self):
      return (
          self.feature_log_prob_[1:]
          if len(self.classes_) == 2
          else self.feature_log_prob_
      )
  
imports: from abc import ABCMeta, abstractmethod
  import numpy
  from scipy.sparse import issparse
  from .base import BaseEstimator, ClassifierMixin
  from .preprocessing import binarize
  from .preprocessing import LabelBinarizer
  from .preprocessing import label_binarize
  from .utils import check_X_y, check_array
  from .utils.extmath import safe_sparse_dot, logsumexp
  from .utils.multiclass import _check_partial_fit_first_call
  from .utils.fixes import in1d
  from .utils.validation import check_is_fitted
  from .externals import six
label_desc: Use image node_id 4 for calling the BaseDiscreteNB obj's underlying member method code with example usage: obj._get_coef() and returns: unknown