Class MultinomialNBScikitsLearnNode

Naive Bayes classifier for multinomial models

This node has been automatically generated by wrapping the ``sklearn.naive_bayes.MultinomialNB`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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
    Whether to learn class prior probabilities or not.
    If false, a uniform prior will be used.

class_prior : array-like, size (n_classes,)
    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

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Perform classification on an array of test vectors X.

This node has been automatically generated by wrapping the sklearn.naive_bayes.MultinomialNB class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : array-like, shape = [n_samples, n_features]

Returns

C : array, shape = [n_samples]

Predicted target values for X

Overrides: ClassifierNode.label

Fit Naive Bayes classifier according to X, y

This node has been automatically generated by wrapping the sklearn.naive_bayes.MultinomialNB class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples]

Target values.

sample_weight : array-like, shape = [n_samples], optional

Weights applied to individual samples (1. for unweighted).

Returns

self : object

Returns self.

Overrides: Node.stop_training