Author: Sari Saba-Sadiya
For students in cse440 introduction for artificial intellegence, spring 2019.
Naive Bayes Classifiers are a powerful tools that leverage Bayesian probability to learn from labeled data and make inference. While they have been becoming less popular in the aftermath of the deep learning tsunami, they remain a favorite among many for their ease of use and their relatively transparent nature (in comparison to neural network black boxes).
This assignment will walk you through reproducing some results achieved by Dr Lillian Lee at the early 2000 using Naive Bayes classifiers.
Contents:
|-> trainData : training data for the Bayesian Network.
|-> testData : testing data for the Bayesian Network.
|-> cse440Bayes.pdf : The assignment guide.
To view the code use: https://nbviewer.jupyter.org/github/sari-saba-sadiya/Naive-Bayes-Classifier-for-Natural-Language/blob/master/naiveBayesCensor.ipynb