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]))