tra_analysis.NaiveBayes

NaiveBayes is a class that holds 4 submodules. The submodules are Naieve Bayes algorithms for classification. There are 4 different "kernels" or methods that can be used in naive bayes classification, they differ in the assumptions they make regarding the distribution of P(x_i｜y). Refer to sklearn's documentation for more information on use.

Given data and labels, each subclass will return a fitted model given the specified kernel type.

Note: After update 3.x, the classness of this module has been removed.

Example Usage

All kernels expect the same required arguments and only differ in their optional parameters. Usage is identical to sklearn.naive_bayes.*.

from tra_analysis import NaiveBayes

data = [[0], [1], [2], [3]]

labels = [0, 0, 1, 1]

NaiveBayes.guassian(data, labels, test_size = 0.25)[0]
NaiveBayes.guassian(data, labels, test_size = 0.25)[1][0]
NaiveBayes.guassian(data, labels, test_size = 0.25)[1][1]

outputs:

[0] (model)

GaussianNB()

[1][0] (cm)

[[0, 0],
 [1, 0]]

[1][1] (cr)

              precision    recall  f1-score   support

           0       0.00      0.00      0.00       0.0
           1       0.00      0.00      0.00       1.0

    accuracy                           0.00       1.0
   macro avg       0.00      0.00      0.00       1.0
weighted avg       0.00      0.00      0.00       1.0