Classification versus Regression

“Machine Learning” is the new devil in the market. Every day we used to listen some fancy words and algorithms .We have heard that people are able to classify the animal images and able to predict the stock prices.

We need to understand few jargon related to ML.

 So , let us start our discussion with the topic Classification Vs Regression. 

Classification :-

The main goal of classification is to predict the target class (Yes/ No). .Suppose,  we want to know whether a student fails or pass. It is a polarity based algorithm.

Examples :- To find whether a mail is spam or not.

                     Whether an image belongs to a cat or a dog.

Types of Classification:-

Binary Classification :- When we have only two target class labels to predict.

Example :- Pass or Fail 

Multi-Class Classification :- When there are more than two class label to predict.

Example :- image classification problems where there are more than thousands classes(cat, dog, fish, car,…). 

Algorithms For Classification:-

•   KNN(K Nearest Neighbor)
•   SVC(Support Vector Classifier)
•  Decision Tree etc.

Regression:- In regression problems, we are trying to predict continuous valued output,.

Given a stock and to predict its value in next few months.

Algorithms For Regression :-

•  Linear Regression
•  SVR( Support Vector Regression) etc

Whenever we find machine learning problem first define whether we are dealing with a classification or regression problem and we can get to know that by analyzing the target variable (Y).