Explain Naïve Bayes Classification Algorithm with a suitable example.

The Naïve Bayes algorithm is a probabilistic machine learning model used for classification tasks. It's based on Bayes' theorem with an assumption of independence between features.

Key aspects:

1. It's called "naïve" because it assumes that features are independent of each other, which is often not the case in real-world scenarios.
2. Despite this simplifying assumption, it often performs well in many complex real-world situations.
3. It's particularly useful for high-dimensional data sets.

The algorithm is based on Bayes' theorem:

P(A|B) = P(B|A) * P(A) / P(B)

Where:

• P(A|B) is the posterior probability
• P(B|A) is the likelihood
• P(A) is the prior probability
• P(B) is the evidence

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