sklearn-MultinomialNB朴素贝叶斯分类器

原型

class sklearn.naive_bayes.MultinomialNB(alpha=1.0, fit_prior=True, class_prior=None)

参数

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.

alpha的说明——

The parameters  is estimated by a smoothed version of maximum likelihood, i.e. relative frequency counting:

where  is the number of times feature  appears in a sample of class  in the training set , and  is the total count of all features for class .

The smoothing priors  accounts for features not present in the learning samples and prevents zero probabilities in further computations. Setting  is called Laplace smoothing, while  is called Lidstone smoothing.

示例

>>> 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]))