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Naive Bayes Classifier

version (87 KB) by Mahmoud Afifi
Naive Bayes Classifier


Updated 04 Jan 2019

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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!

Cite As

Mahmoud Afifi (2022). Naive Bayes Classifier (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2014a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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