What is conditional probability in AI?
In probability theory, conditional probability is a measure of the probability of an event given that (by assumption, presumption, assertion or evidence) another event has occurred. For example, the probability that any given person has a cough on any given day may be only 5%.
How does the naive Bayesian classification works explain?
Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other. To start with, let us consider a dataset.
Which is the first probability in naive Bayes?
The first probability, P (IsCat), is the prior and the second probability is the scaler — and as we just learned, the product of prior and scaler is a joint probability: So we can rewrite our joint probability as: Almost there! This is where naive Bayes’ simplifying assumption comes to save the day.
Is it worth learning the naive Bayes classifier?
While it might lack some of the hype and firepower of more recently developed algorithms, it’s a robust and versatile tool that has withstood the test of time ( also its shortcomings are very well-understood which is often just as useful or even more so when building models) — so it’s definitely worth learning more about. What Does It Do?
What is the numerator of the Bayes theorem?
P (A|B,C) is the probability of A occurring given that B and C have already occurred. From Bayes’ Theorem and general probability we know that: So the numerator of Bayes’ theorem (the product of the scaler and prior) is a joint probability.
How is the normalizer used in Bayes classifier?
If only a small proportion are, then it helps lower our belief that we are dealing with a cat. The normalizer is used to adjust the numerator — this adjusts the calculated probability for the rarity of the evidence. The ratio between the scaler and the normalizer is pretty important to understand.