Supervised Learning: How Classification Model Works

Have you ever wondered how your phone detects spam texts and automatically sends them to the junk folder? This is an example of classification in supervised learning—a branch of machine learning.
In this article, we'll delve into supervised learning, its models, and real-world applications.

What is Supervised Learning?

This branch of machine learning concentrates with learning patterns through connecting the relationship between variables and known outcomes and working with labelled datasets. The patterns from said datasets creates a model that can reproduce the same underlying rules with new data (testing data).

Under Supervised learning are two branches, classification and regression, however, our main focus is classification.

Classification

Classification algorithms can be used to create outputs that are restricted to a limited set of values. Data is trained to identify the input and assign classes accordingly.

Example:

• Identifying an email as spam or not.
• Transaction being specified as fraudulent or not

Types of Classification Tasks

• Binary Classification- two possible classes
• Multi-Class- more than two classes
• Multi-Label- multiple classes at the same time. Example:

Models used for classification

• Logistic Regression- typically used for binary classification to predict two discrete classes, for example pregnant or not pregnant.
• K-Nearest Neighbors- a supervised learning technique used to classify new data points based on relationship to closest data points.
• Decision Trees- breaks down and explains how classification is done using neat visual representation, hence the name decision trees. It begins at the root node from whence branches emerge, otherwise known as leaf nodes.
• Random Forest- this is a collection of multiple decision trees.

Real-world applications of Classification

Education: Determining whether a student should be awarded a scholarship
Banking: Determining whether a customer is eligible for a loan.
Agriculture: Predicting the suitable weather for optimum production of certain crops.

Final Thoughts

Classification is more than just a theoretical concept; it's a powerful tool for making data-driven decisions. From optimizing business strategies like product recommendations to enhancing your grasp of machine learning's inner workings, mastering this technique is a fundamental inbuilding intelligent systems.