Classification

Definition of Classification

Classification: The task of classification is to take a set of observations and assign one of a pre-determined number of classes to each observation. Classification is a technique used in data science to group similar objects together.

What is Classification used for?

Classification is a task in machine learning and data science that is used for sorting data into different categories. This is typically done by analyzing the differences between data points and then grouping them according to their similarities, such as semantic meaning or patterns. Classification has a wide range of applications, from medical diagnosis, to fraud detection and automatic image tagging.

In medical diagnosis, for example, doctors might use classification algorithms to compare patient symptoms with known symptoms of diseases in order to make a diagnosis. Similarly, automatic image taggers can detect objects in an image and classify them as cars, animals or whatever it was trained on. This can be used in various areas like autonomous driving or surveillance systems. Furthermore, fraud detection systems use classification algorithms to identify transactions that might be fraudulent based on past behavior or certain patterns observed in the transaction data.

The goal of classification is to accurately predict which category new data points belong to given the features associated with them. A variety of algorithms are available for this purpose from basic k-nearest neighbors classification (KNN) to support vector machines (SVMs), random forests and neural networks. The choice of algorithm depends on the dataset at hand and it’s characteristics, such as its size, complexity and feature distribution. Additionally, evaluation metrics like accuracy or F1 score are often used to measure the performance of classification models so they can be optimized accordingly during development stages.