A blog for computer science passionates.

Wednesday 28 March 2018

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.

Monday 5 March 2018

Hello Friends,

In this article, we are going to learn about Supervised Learning in detail. In previous article we discussed about all the types of learning in brief. From which we learn more about Supervised Learning. So, Let's take a tour to Supervised Learning.

Supervised Learning is most popular and successful learning algorithm. In supervised learning, there is a teacher to train set of data. i.e. the set of data learns under supervision of an instructor or teacher. we all know about supervised learning, it is something like humans learning methods. In supervised learning, there is input as well as output data. such as, X is my input data, Y becomes output data.something like,
Y=f(X)

Here, we have input data X, it is training data. so, here we have a teacher who give training to our input data X, and X make a prediction and it is corrected by the teacher and represent some output Y. This algorithm is known as Supervised Learning. In this algorithm we try to make accurate prediction to generate unseen or new data that has never seen before.

We have two types of Supervised Machine Learning algorithms,
  • Classification
  • Regression
In classification, we predict a different class and give some label to that class.For example, if we have different flowers, we classify this data in different other class based on its' different types and smell and look such as,Rose, Water lily, Sun - Flower, Jasmine, Orchid etc. we just classify different flowers as per their category.
Let's take demo example of classification.
First of all I have this sample data set.
Figure1. Data Set
I have this .csv file as my data set. I put some flower name and set it's color and width and height.
Figure2. Classification using Flower name and It's Color.

In this example, I classify flower through their color.

In regression,  we have some real values related data, i.e. Monthly - Income, or Predicting a price of a car or home etc, and we get the output in real values, we predict based on real values is regression.

This is supervised learning, hope you get supervised learning easily,
In next article I will discuss more interesting things related to Machine Learning, till then Enjoy...