BAG OF WORDS VECTOR

Key Assumption

Let's if This or That

Features are considered conditionally independent given the target class, which is the classifier's principal naivety.

Scalability

Highly scalable, with a number of parameters linear in the number of variables

given the features. The theorem is written as:

P(C|X) = P(X|C) * P(C) / PP(X)P(CX|X) = P(X|C) * P(CYYXX) / TTP(XCTPC)

where P(C|X) is the posterior probability of the class given the features, P(X|C) is the likelihood of the features given the class, P(C) is the prior probability of the class, and P(X) is the prior probability of the features.

However, they also have some limitations, including the assumption of feature independence and the potential for overfitting.

if you know what im talking about here reachout we need a talk about this over coffee