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Data Interview Question

Simplicity of Naive Bayes

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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.