When we describe the house, we usually use the attributes such as residential area, house type, decoration type, etC. If we use plain With Bayes as a model, we assume that there is no relationship…

Question

Options

• ATRUE
• BFALSE

• A
  78% (25)
• B
  22% (7)

Explanation

Option A is correct because Naive Bayes (the "plain Bayes" model) operates under the naive conditional independence assumption - it treats all features as statistically independent of one another given the class label. For house prediction, this means the model assumes that knowing the residential area tells you nothing extra about the house type or decoration, and vice versa. In reality these attributes often correlate (e.g., larger areas tend to correlate with certain house types), but Naive Bayes deliberately ignores these relationships to keep computation tractable.

Option B is wrong because claiming Naive Bayes does account for relationships between attributes would describe a more sophisticated model (like a Bayesian Network or a model with feature interaction terms), not Naive Bayes.

Memory tip: The word "Naive" is the key - the model is "naive" precisely because it naively ignores all inter-feature dependencies, treating each attribute as if it were generated in isolation.

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