Explain Bayes classifier with the help of a suitable example. Also discuss its properties.

The Bayes classifier is a probabilistic model used in digital image processing and computer vision for classification tasks. It is based on Bayes' Theorem, which calculates the posterior probability of a class given the observed features. The classifier assigns a given input to the class with the highest posterior probability.

Mathematically, Bayes' Theorem is expressed as:

P(Ck∣x)=P(x∣Ck)⋅P(Ck)P(x)P(C_k | x) = \frac{P(x | C_k) \cdot P(C_k)}{P(x)}

Here, P(Ck∣x)P(C_k | ______ __________ _____ _______ _______.
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