Machine Learning - Bayes Optimal Classifier Problem Solved Step by Step Machine Learning by Vidya Mahesh Huddar

Bayes Optimal Classifier Problem Solved Step by Step Machine Learning by Vidya Mahesh Huddar In this video, we will solve an example problem based on the Bayes Optimal Classifier, which is an important concept in Machine Learning and probabilistic classification. We will learn how to compute probabilities and determine the best classification for a new instance using hypothesis probabilities. Consider the following data. The set of possible classifications for the new instance is: V = { Positive (⊕), Negative (⊖) } We are given three hypotheses: For hypothesis h₁ P(h₁|D) = 0.4, P(⊖|h₁) = 0, P(⊕|h₁) = 1 For hypothesis h₂ P(h₂|D) = 0.3, P(⊖|h₂) = 1, P(⊕|h₂) = 0 For hypothesis h₃ P(h₃|D) = 0.3, P(⊖|h₃) = 1, P(⊕|h₃) = 0 We need to calculate: Σ P(⊕|hᵢ) P(hᵢ|D) Σ P(⊖|hᵢ) P(hᵢ|D) The following concepts are discussed: ______________________________ bayes optimal classifier, bayes optimal classifier numerical, bayes optimal classifier example, bayes optimal classifier solved example, ******************************** Follow Us on: 1. Blog / Website: https://www.vtupulse.com/ 2. Download Final Year Project Source Code: https://vtupulse.com/download-final-year-projects/ 3. Like Facebook Page: https://www.facebook.com/VTUPulse 4. Follow us on Instagram: https://www.instagram.com/vtupulse/ 5. Like, Share, Subscribe, and Don't forget to press the bell ICON for regular updates