Multinomial Naive Bayes with Laplace ( ADD 1 ) Smoothing | How we make dictionary? | Past Paper Q

Описание: 📘 Applied Machine Learning Playlist:
   • CS4014 - Applied Machine Learning  

Multinomial Naive Bayes is a powerful Machine Learning algorithm commonly used for text classification, spam detection, and NLP tasks. A key step in this algorithm is creating the dictionary (vocabulary) and applying Laplace (Add-1) smoothing to handle zero probabilities.

In this video, we solve a past paper question step by step, explaining how to create the dictionary, calculate probabilities, and apply Laplace smoothing in Multinomial Naive Bayes.

You will learn:

What is Multinomial Naive Bayes

How to create the dictionary (vocabulary)

Why dictionary size is important

What is Laplace (Add-1) smoothing

How smoothing prevents zero probability

Step-by-step solved past paper example

Prior probability and likelihood calculation

How prediction is made in Multinomial Naive Bayes

Applications in NLP, spam filtering, and text classification

This lecture is part of the Applied Machine Learning playlist and is ideal for:

Machine Learning beginners

Data Science students

AI students

University exam and past paper preparation

Interview preparation

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