Bayesian Classifier

A statistical classifier called Naive Bayesian

classifier is discussed. This classifier is based on the Bayes’ Theorem and the maximum posteriori hypothesis. The naive assumption of class conditional independence is often made to reduce the computational cost.

”Naive”

Naive Bayesian classifiers assume that the effect of an attribute value on a given class is independent of the values of the other attributes. This assumption is called class conditional independence. It is made to simplify the computation involved and, in this sense, is considered ”naive”.

P(H|X) is the a posteriori probability of H conditioned on X.

Fox example, suppose our data samples have attributes: age and income, and that sample X is a 35-year-old customer with an income of $40,000. Suppose that H is the hypothesis that our customer will buy a computer. Then P(H|X) is the probability that customer X will buy a computer given that we know the customer’s age and income.

In contrast,P(H) is the prior probability , or a priori probability, of H .For our example, this is the probability that any given customer will buy a computer,regardless of age, income, or any other information, for that matter. The posterior probability, P(H|X), is based on more information (e.g., customer information) than the prior probability, P(H), which is independent of X.

- Studies comparing classification algorithms have found that the naive Bayesian classifier to be comparable in performance with decision tree and selected neural network classifiers.

- Bayesian classifiers have also exhibited high accuracy an speed when applied to large databases.

MySQL vs MongoDB

Database

GUI

Exception Handling

Database

Timer

Learning Outcomes

How to use MySQL

How to design a Graphical User Interface

How does Naive Bayes work

Internal Working of Python Operations like Division

How to implement timers

Measuring Time Complexity

Comparison with Java Implementation

Time Complexity -

Time Complexity of both the implementations will be the same as the underlying logic of the program remains the same whether it is implemented in Java or Python. So the Time Complexity of both in terms of Big O Notation is O(n).

Comparison with Java Implementation

Speed -

It is difficult to compare the speed of the Java Program and the Python Program because we used a GUI for Python but the Java Program used CLI.
 

But still comparing the inner calculations the time taken for small dataset is very similar in both but for bigger databases Java is faster than Python.