Classification

Hello Friends,
In the previous article, we have discussed supervised learning algorithms and even we have seen a simple classification example in python, but now in this article, we are going to take deep dive into Classification, algorithm,
Actually, in the Supervised Learning algorithm, there is a data-set and from that data-set, we want to learn to classify. Now say, for example, I have some data of students and if I made a survey about how many students want to learn computer programming, so for us, those students who want to learn computer programming are positive examples, and others are negative examples. We find a class of students who want to learn computer programming so, we should consider positive examples only for that we need to make a prediction based on knowledge extraction.

In classification sometimes we have either true/false or Yes / No or Male / Female type of data, i.e. in our above example, students who want to learn computer programming is a positive example, so those who want to learn computer programming says YES. So,

This type of Classification is known as binary classification. and is used to classify two classes on the basis of a classification rule.

Other types of classifications are

• Multi-Class Classification
• Multi-Label Classification

Sometimes, these both becomes ambiguous,  So, let's differentiate these both,

Multi-Class Vs. Multi-Label Classification,

Here, let's take an example, Consider the word Animal, in which we have Multiple - Classes such as,
There are Birds, Mammals, etc. And Birds class has lots of different types of Birds such as parrot, peacock,  sparrow, etc. Here, parrot, pea-cock are different labels.

Fig. 1 Multi-Class Classification

(Image Source: pixabay)

In the above pic, you can see three different classes, and with different animals, you can classify these classes with different labels of animals such as class wild - animal has tiger, monkey, etc. labels.

So, this is Classification and it's type. In the next post, we will see regression.
Enjoy with Machine Learning.