May 20, 2023

Classification is a fundamental concept in artificial intelligence and machine learning that involves categorizing input data into pre-defined classes or categories. The primary goal of classification is to learn a decision boundary that separates data points into different classes based on their features or attributes.

Types of Classification

There are two main types of classification: binary classification and multi-class classification.

Binary Classification

In binary classification, the input data is divided into two classes or categories. This can be represented as a simple decision boundary that separates the two classes. For example, a binary classification problem could involve determining whether an email is spam or not spam, based on the email’s features such as subject line, content, and sender information.

Multi-class Classification

In multi-class classification, the input data is divided into more than two classes or categories. This type of classification problem is more complex than binary classification, as it requires learning multiple decision boundaries to separate the different classes. For example, a multi-class classification problem could involve classifying handwritten digits into numbers 0-9.

How Classification Works

Classification involves two main steps: training and testing.


In the training step, a machine learning model is trained on a labeled dataset to learn the decision boundary that separates the different classes. The labeled dataset contains input data with their corresponding class labels. The model is trained by feeding it the labeled data and adjusting its parameters to minimize the error between its predicted outputs and the true labels.

One common algorithm used for binary classification is logistic regression. Logistic regression works by fitting a logistic curve to the data, which models the probability of a data point belonging to a particular class. Another common algorithm used for multi-class classification is the support vector machine (SVM). SVM works by finding the hyperplane that maximally separates the different classes.


In the testing step, the trained model is evaluated on a new, unseen dataset to measure its accuracy in classifying the input data. The new dataset is typically split into two parts: a test set and a validation set. The test set is used to evaluate the model’s accuracy, while the validation set is used to fine-tune the model’s parameters and prevent overfitting.

Applications of Classification

Classification has many practical applications in various fields such as healthcare, finance, and cybersecurity. Here are a few examples:


Classification can be used in healthcare to diagnose diseases based on patient data such as symptoms, medical history, and lab results. For example, a machine learning model could be trained to diagnose breast cancer from mammogram images.


Classification can be used in finance to detect fraud or predict credit risk. For example, a machine learning model could be trained to detect fraudulent credit card transactions based on transaction history and other features.


Classification can be used in cybersecurity to detect malicious software or network attacks. For example, a machine learning model could be trained to classify network traffic as legitimate or malicious based on traffic patterns and other features.