Naive Bayes is the most simple algorithm that you can apply to your data. As the name suggests, here this algorithm makes an assumption as all the variables in the dataset is “Naive” i.e not correlated to each other.

Naive Bayes is a very popular classification algorithm that is mostly used to get the base accuracy of the dataset.

Explain like I am five

Let’s assume that you are walking on the playground. Now you see some red object in front of you. This red object can be a bat or a cat or a ball. You will definitely assume that it will be a ball. But why so?

Let’s us think you are making a machine and you have given the task as above to classify an object in between bat, ball and a cat. At first you will think of creating a machine that will identify the characters of the object and then map it with your classification objects such that if an object is a circle then it will be a ball or if the object is living-being then it will be a cat or in our case, if our object is red then it is most probable that it will be a ball.

Why so? because from our childhood we have seen a red ball but a red cat or a red bat is very unlikely to our eyes.

So in our case, we can classify an object by mapping its features with our classifier individually. As in our case, this red color was mapped with a bat, a cat, and a ball, but eventually, we get the most probability of red object with a ball and therefore we classified that object with a ball.