The files here are:
1- load_data: import data from the csv file
2- visualization: print histograms of features' dist. over the two classes in the training data in a folder called visualization.
3- estimate_: estimate the model of given data
4- classify_: classify based on the model and data
5- testing: test the Naive classifer using alpha=1:0.1:1000 and print a figure called (accuracy 1-1000.pdf) in the visualization folder
6- InspectTheModel: try to measure the impact of each feature value per class
7- jointProb: calc joint probability of two given feature values given a class
8- mutualInformation: calculate the mutual information over the training data to drive the most likely dependent pair of features.
9- testingBonus: test the Naive classifier using the candidate pair of features.
To run a demo, run testing.m but change the start, step, and end as you want!
Mahmoud Afifi (2019). Naive Bayes Classifier (https://www.mathworks.com/matlabcentral/fileexchange/64569-naive-bayes-classifier), MATLAB Central File Exchange. Retrieved .