Supervised Learning — Naive Bayes Algorithm
This article explains the Naive Bayes which is one of the machine learning algorithms.
The structure of Naive Bayes is similar to linear models but gives faster results compared to linear models. However, the price of being fast is that the generalization performance is worse than linear models. The reason why Naive Bayes algorithm is efficient is based on the idea behind of it. Naive Bayes relies on collecting simple statistical knowledge by looking at the parameters individually for each feature. First of all, let’s explain the theoretical knowledge and then continue with what kind of statistical data is collecting.
Bayes Theorem
Bayes’ theorem is the relationship between the conditional probabilities and marginal probabilities for one random event X and another random event Y (if there is the extant probability for Y) occurring during a stochastic process. The mathematical model of this definition is as follows:
Now, let’s implement this theoretical knowledge with an example. Dataset consists of 11 point which are 6 of them represent software developer and 5 of them represent Civil Engineer. Structure of the data…
