WebOct 12, 2024 · It’s a simple method to construct classifiers, suppose we have two bakery’s (A and B) that produce bread. Naive Bayes helps us to answer questions such as “ what’s the probability that bakery B over-bakes their bread ?”. It’s known as “ naive ” Bayes because it often ignores the assumption that features are independent. WebThe bayes risk under the prior π is defined as follows: Bπ(ˆθ) = ∫R(θ, ˆθ)π(θ)dθ. I don't quite get what the prior π is doing and how I should interpret it. If I have a risk function R(θ, ˆθ) and plot it, intuitively I would take its area as a criterion to judge how "strong" the risk is over all possible values of θ.

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