Simplicity of Naive Bayes

Requirements Clarification & Assessment

1. Understanding the Question:

   • The question seeks to elucidate why the Naive Bayes classifier is termed "naive."
   • It is important to explain the underlying assumptions and their implications on the algorithm's performance.

2. Key Concepts to Cover:

   • Explanation of Bayes' theorem and its role in classification.
   • The assumption of feature independence and why it is considered naive.
   • The computational benefits and limitations of this assumption.

3. Target Audience:

   • Interviewers with an understanding of machine learning concepts.
   • Individuals assessing the candidate's depth of knowledge in probabilistic models.

4. Expectations:

   • Clear explanation of why the assumption is naive.
   • Examples or scenarios where Naive Bayes performs well or poorly.
   • Discussion on the trade-offs of using Naive Bayes versus other models.