Classification and its Use Cases in Machine Learning
In this article, I am going to discuss What is Classification and its Use Cases in Machine Learning with Examples. Please read our previous article where we discussed Model Evaluation for Regression in Machine Learning with Examples.
What is Classification and its Use Cases in Machine Learning?
Classification is the process of dividing a set of data into categories. It can be done on both structured and unstructured data. Predicting the class of provided data points is the first step in the procedure. Target, label, and categories are all terms used to describe the classes.
The task of approximating the mapping function from discrete input variables to discrete output variables is classified as predictive modeling. The basic goal is to figure out which category or class the new data belongs to. Let’s have a look at a basic example to help us comprehend.
The type of content of an online article can be classified using Machine Learning Classification algorithms. In this situation, the classifier requires training data in order to comprehend how the input variables are related to the class. Once the classifier has been properly trained, it can be used to determine if an article’s content is of type – sports or automotive, or science. There are a total of three types of classification tasks –
- Binary Classification – Classification problems with two class labels are referred to as binary classification. Example – Email Spam Classification (spam or not spam).
- Multi-class Classification – Classification jobs with more than two class labels are referred to as multi-class classification. Example – Classification of types of music.
- Multi-label Classification – Classification tasks with two or more class labels, where one or more class labels can be anticipated for each case, are referred to as multi-label classification. Consider object detection, where an image may contain numerous things, and a model can predict the existence of multiple known objects in the scene, such as “bicycle,” “apple,” “person,” and so on.
Use Cases of Classification in Machine Learning-
Predicting customer behavior – Customers can be split into groups based on their shopping habits, browsing habits in online stores, and other characteristics. For example, classification models can be used to determine whether or not a customer is likely to purchase additional items. If the categorization model indicates that they will make more purchases, you may want to make special offers and discounts available to them. Alternatively, if it has been determined that they are likely to forsake their buying habits in the near future, you may choose to store their information for further use.
Document classification – A multinomial classification model can be used to divide documents into distinct categories.
Image classification – A multinomial classification model can be used to divide photos into various categories.
Product categorization – A multinomial classification can be used to categorize products sold by multiple shops in the same categories, regardless of the categories assigned to them by the individual merchants. This use case can help eCommerce aggregators.
Malware classification – Malware can be classified using a multinomial classification system based on similar malware features. Malware classification is incredibly useful in choosing the best line of action for combating and preventing malware for security experts.
In the next article, I am going to discuss the Decision Tree in Machine Learning with Examples. Here, in this article, I try to explain Classification and its Use Cases in Machine Learning with Examples. I hope you enjoy this Classification and its Use Cases in Machine Learning with Examples article.
About the Author: Pranaya Rout
Pranaya Rout has published more than 3,000 articles in his 11-year career. Pranaya Rout has very good experience with Microsoft Technologies, Including C#, VB, ASP.NET MVC, ASP.NET Web API, EF, EF Core, ADO.NET, LINQ, SQL Server, MYSQL, Oracle, ASP.NET Core, Cloud Computing, Microservices, Design Patterns and still learning new technologies.