Class BernoulliNBScikitsLearnNode

Naive Bayes classifier for multivariate Bernoulli models.

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

Like MultinomialNB, this classifier is suitable for discrete data. The
difference is that while MultinomialNB works with occurrence counts,
BernoulliNB is designed for binary/boolean features.

Read more in the :ref:`User Guide <bernoulli_naive_bayes>`.

**Parameters**

alpha : float, optional (default=1.0)
    Additive (Laplace/Lidstone) smoothing parameter
    (0 for no smoothing).

binarize : float or None, optional
    Threshold for binarizing (mapping to booleans) of sample features.
    If None, input is presumed to already consist of binary vectors.

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]
    Log probability of each class (smoothed).

``feature_log_prob_`` : array, shape = [n_classes, n_features]
    Empirical log probability of features given a class, P(x_i|y).

``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(2, size=(6, 100))
>>> Y = np.array([1, 2, 3, 4, 4, 5])
>>> from sklearn.naive_bayes import BernoulliNB
>>> clf = BernoulliNB()
>>> clf.fit(X, Y)
BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)
>>> print(clf.predict(X[2:3]))
[3]

**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/the-bernoulli-model-1.html

A. McCallum and K. Nigam (1998). A comparison of event models for naive
Bayes text classification. Proc. AAAI/ICML-98 Workshop on Learning for
Text Categorization, pp. 41-48.

V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with
naive Bayes -- Which naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS).

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.BernoulliNB 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.BernoulliNB 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