Array-Like Shape (N_Samples,) vs [N_Samples] in Sklearn Documents

You can use a simple example to test this:

import numpy as np
from sklearn.naive_bayes import GaussianNB

#create some data
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
Y = np.array([1, 1, 1, 2, 2, 2])

#create the model and fit it
clf = GaussianNB()
clf.fit(X, Y)

#check the type of some attributes
type(clf.class_prior_)
type(clf.class_count_)

#check the shapes of these attributes
clf.class_prior_.shape
clf.class_count_

Or more advanced searching:

#verify that it is a numpy nd array and NOT a list
isinstance(clf.class_prior_, np.ndarray)
isinstance(clf.class_prior_, list)

Similarly, you can check all the attributes.

Results

numpy.ndarray

numpy.ndarray

(2,)

array([ 3.,  3.])

True

False

The results indicate that these atributes are numpy nd arrays.