class_docstr: Naive Bayes classifier for multinomial models
  
  The multinomial Naive Bayes classifier is suitable for classification with
  discrete features (e.g., word counts for text classification). The
  multinomial distribution normally requires integer feature counts. However,
  in practice, fractional counts such as tf-idf may also work.
  
  Read more in the :ref:`User Guide <multinomial_naive_bayes>`.
  
  Parameters
  ----------
  alpha : float, optional (default=1.0)
      Additive (Laplace/Lidstone) smoothing parameter
      (0 for no smoothing).
  
  fit_prior : boolean, optional (default=True)
      Whether to learn class prior probabilities or not.
      If false, a uniform prior will be used.
  
  class_prior : array-like, size (n_classes,), optional (default=None)
      Prior probabilities of the classes. If specified the priors are not
      adjusted according to the data.
  
  Attributes
  ----------
  class_log_prior_ : array, shape (n_classes, )
      Smoothed empirical log probability for each class.
  
  intercept_ : property
      Mirrors ``class_log_prior_`` for interpreting MultinomialNB
      as a linear model.
  
  feature_log_prob_ : array, shape (n_classes, n_features)
      Empirical log probability of features
      given a class, ``P(x_i|y)``.
  
  coef_ : property
      Mirrors ``feature_log_prob_`` for interpreting MultinomialNB
      as a linear model.
  
  class_count_ : array, shape (n_classes,)
      Number of samples encountered for each class during fitting. This
      value is weighted by the sample weight when provided.
  
  feature_count_ : array, shape (n_classes, n_features)
      Number of samples encountered for each (class, feature)
      during fitting. This value is weighted by the sample weight when
      provided.
  
  Examples
  --------
  >>> import numpy as np
  >>> X = np.random.randint(5, size=(6, 100))
  >>> y = np.array([1, 2, 3, 4, 5, 6])
  >>> from sklearn.naive_bayes import MultinomialNB
  >>> clf = MultinomialNB()
  >>> clf.fit(X, y)
  MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)
  >>> print(clf.predict(X[2:3]))
  [3]
  
  Notes
  -----
  For the rationale behind the names `coef_` and `intercept_`, i.e.
  naive Bayes as a linear classifier, see J. Rennie et al. (2003),
  Tackling the poor assumptions of naive Bayes text classifiers, ICML.
  
  References
  ----------
  C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
  Information Retrieval. Cambridge University Press, pp. 234-265.
  http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html
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code: def __init__(self, alpha=1.0, fit_prior=True, class_prior=None):
      self.alpha = alpha
      self.fit_prior = fit_prior
      self.class_prior = class_prior
  
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 1 to create a new MultinomialNB object from inherited base classes: BaseDiscreteNB with example: obj = MultinomialNB(alpha, fit_prior, class_prior)