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What is the assumption of conditional independence in naive Bayes classifier?
Naive Bayes classifier assume that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. This assumption is called class conditional independence. P(c|x) is the posterior probability of class (target) given predictor (attribute).
What is the assumption made in naive Bayes algorithm?
What is Naive Bayes algorithm? It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
When can a feature independence assumption be reasonable?
A1: It is reasonable when the features are conditionally independent given classification, or at least reasonably independent, still works with little dependence. However if features a strictly correlated and not cancel each other out, NB assumption do not perform as good.
Which is correct form of Bayes Theorem?
Formula for Bayes’ Theorem P(A|B) – the probability of event A occurring, given event B has occurred. P(B|A) – the probability of event B occurring, given event A has occurred. P(B) – the probability of event B.
What is the basic assumption of naive Bayes?
The basic assumption in Naïve Bayes is one of conditional independence between all independent variable features. Conditional independence ensures that how one feature affects an outcome in no way interacts with how another variable affects the same outcome.
Why is the Gaussian naive Bayes classifier important?
The Gaussian Naïve Bayes classifier, or what is usually just called Naïve Bayes, is a wonderfully simple approach that often returns very accurate and stable models with very small sample sizes.26 The reason that Naïve Bayes often works so well is that it simplifies predictive modeling problems to avoid the curse of dimensionality.
How are implicit relr models similar to naive Bayes?
Implicit RELR models have some similarity to Naïve Bayes in that their regression coefficients can be proportional to t values at very small samples with a large number of independent variables.
What is the naive assumption of P ( O | C I )?
To reduce the computation of evaluating P (O | C i ), the naive assumption of class independence is made. This presumes that the attribute values are conditionally independent of one another, given the class label of the object (that is, there are no dependent relationships among the attributes).