The document provides an overview of the Naïve Bayes classification algorithm, detailing its foundations in Bayesian theory and its application in supervised machine learning for classification tasks. It discusses the workflow involved in data preparation, training, and prediction, as well as the different types of Naïve Bayes classifiers suited for various data forms. Additionally, it highlights the advantages and disadvantages of the method, along with its applications in spam filtering, sentiment analysis, document classification, and recommendation systems.

1. NAÏVE
BAYES
CLASSIFICATI
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Made by: Zenith Acharya
Kathmandu Model College
2023-12-16

2. Introduction
Contextualizing Machine Learning:
Machine Learning (ML) is the science of creating
algorithms that allow systems to learn patterns from
data without explicit programming.
Supervised Learning & Classification:
Supervised learning deals with labelled data to train
models that make predictions or classifications.
Classification is the process of categorizing data into
classes or groups based on its features.
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3. Understanding Naive Bayes
Algorithm
Fundamentals of Naive Bayes
Derived from Bayes' theorem, it's a probabilistic classifier that assumes
independence between features.
Calculates probabilities of class membership given input features.
Bayes' Theorem Formula
Where A and B are events, P(A|B) is the probability of A given B.
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4. Naive Bayes Workflow
Data Preparation Phase
Collection of labelled training data.
Pre-processing involves data cleaning, handling missing
values, and feature extraction.
Training Stage
Estimating probabilities of each class occurrence.
Computing conditional probabilities of features given the
class.
Prediction Process
Applying Bayes' theorem to calculate the probability of
each class given new data.
Selecting the class with the highest probability as the
predicted outcome.
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5. Gaussian Naive Bayes
Assumes features follow a Gaussian (normal) distribution.
Suitable for continuous data.
Multinomial Naive Bayes
Primarily used for text classification.
Works well with multiple discrete features, often in the
form of word counts.
Types of Naive Bayes
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6. Bernoulli Naive Bayes
Appropriate for binary or Boolean features.
Particularly useful for document classification tasks
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7. Advantages and Disadvantages
Advantages Disadvantages
Simplicity
Assumption of feature
independence
Sensitivity to irrelevant
features
Scalability
Efficiency
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8. Applications
Spam Filtering
Identifying and
filtering out
unwanted spam
emails.
Sentiment Analysis
Determining the
sentiment or emotion
expressed in text data.
Document
Classification
Categorizing
documents into
predefined classes or
categories.
Recommendation
Systems
Generating
personalized
recommendations
based on user
preferences and
behavior.
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https://www.slideshare.net/ZenithAcharya/naive-bayes-
classification
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