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-scienceClassification Algorithms

Classification Algorithms

A type of machine learning algorithm used to predict a categorical label or class that an instance belongs to, based on its features, including binary and multi-class classification using algorithms such as decision trees, logistic regression, and support vector machines

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

Overview

Classification algorithms are essential tools in machine learning that enable us to categorize data into distinct classes. They are widely used in various applications, from spam detection in emails to diagnosing medical conditions. Understanding the different types of classification algorithms, suc...

Quick Links

Study FlashcardsQuick SummaryPractice Questions

Key Terms

Classification
The process of predicting the category of data points.

Example: Classifying emails as spam or not spam.

Supervised Learning
A type of machine learning where the model is trained on labeled data.

Example: Training a model with labeled images of cats and dogs.

Decision Tree
A flowchart-like structure used for decision-making in classification.

Example: Using a decision tree to classify whether a fruit is an apple or orange.

Support Vector Machine
A classification algorithm that finds the best boundary between classes.

Example: Separating different species of flowers based on petal length.

Accuracy
The ratio of correctly predicted instances to the total instances.

Example: An accuracy of 90% means 90 out of 100 predictions were correct.

Precision
The ratio of true positive predictions to the total positive predictions.

Example: If 8 out of 10 predicted spam emails are actually spam, precision is 0.8.

Related Topics

Regression Algorithms
Regression algorithms predict continuous outcomes rather than categories.
intermediate
Clustering Algorithms
Clustering algorithms group similar data points without predefined labels.
intermediate
Neural Networks
Neural networks are a set of algorithms modeled after the human brain, used for complex classification tasks.
advanced

Key Concepts

Supervised LearningDecision TreesSupport Vector MachinesNeural Networks