Seekh Logo

AI-powered learning platform providing comprehensive practice questions, detailed explanations, and interactive study tools across multiple subjects.

Explore Subjects

Sciences
  • Astronomy
  • Biology
  • Chemistry
  • Physics
Humanities
  • Psychology
  • History
  • Philosophy

Learning Tools

  • Study Library
  • Practice Quizzes
  • Flashcards
  • Study Summaries
  • Q&A Bank
  • PDF to Quiz Converter
  • Video Summarizer
  • Smart Flashcards

Support

  • Help Center
  • Contact Us
  • Privacy Policy
  • Terms of Service
  • Pricing

© 2025 Seekh Education. All rights reserved.

Seekh Logo
HomeHomework Helpcomputer-scienceSpam Classification Models

Spam Classification Models

Spam classification models are a type of machine learning model that encodes learned patterns into its weights to identify spam emails. These models use complex sets of rules to make predictions on new, unseen data in real-time. The significance of spam classification models lies in their ability to improve email filtering systems, reducing unwanted emails and enhancing user experience.

intermediate
3 hours
Computer Science
0 views this week
Study FlashcardsQuick Summary
0

Overview

Spam classification models are essential tools in managing digital communication, helping to filter out unwanted messages and protect users from scams. By leveraging machine learning techniques, these models analyze patterns in email data to distinguish between spam and legitimate content. Understan...

Quick Links

Study FlashcardsQuick SummaryPractice Questions

Key Terms

Spam
Unwanted or unsolicited messages, often sent in bulk.

Example: Promotional emails that you did not sign up for.

Machine Learning
A subset of artificial intelligence that enables systems to learn from data.

Example: Using past email data to predict if a new email is spam.

Feature Extraction
The process of transforming raw data into a format suitable for modeling.

Example: Converting email text into numerical vectors.

Naive Bayes
A simple probabilistic classifier based on applying Bayes' theorem.

Example: Classifying emails as spam or not based on word frequency.

Support Vector Machine (SVM)
A supervised learning model that analyzes data for classification.

Example: Separating spam and non-spam emails using hyperplanes.

TF-IDF
Term Frequency-Inverse Document Frequency, a statistic that reflects the importance of a word in a document.

Example: Identifying key terms in spam emails.

Related Topics

Natural Language Processing
The field of AI that focuses on the interaction between computers and human language.
advanced
Data Mining
The process of discovering patterns in large datasets.
intermediate
Deep Learning
A subset of machine learning that uses neural networks for complex tasks.
advanced

Key Concepts

Machine LearningNatural Language ProcessingFeature ExtractionClassification Algorithms