In this assignment; we implement two Naive Bayes event models, Bernoulli and Multinomial. Bernoulli and Multinomial Naive Bayes classifiers are trained and tested on different datasets. We present the accuracy scores for both Bernoulli and Multinomial Naive Bayes classifiers on different datasets and compare them to see which model performed better.
This project focuses on implementing two different classifiers and make comparison between them. The classifiers employed in this project are both Naive Bayes models. We implement Bernoulli event model and Multonimial event model Naive Bayes classifiers for this project. The detailed implementation of both classifiers explained with detail in the Methods section. After implementing the Bernoulli and Multinomial Naive Bayes classifiers, we present the datasets we experimented on. Naive Bayes based classification models are based on some assumptions on the data, therefore the type of datasets we use would affect the accuracy of both models. Hence, we provide some information about the datasets in the following section to illuminate the behavior of classifiers