"Understanding the Basics of Classification in Machine Learning"


Share post:

Machine learning, a branch of artificial intelligence, relies on mathematical models and algorithms to draw insights from statistics. Among its various strategies, ‘classification’ is one of the most fundamental. In its most basic sense, machine learning classification involves categorising data points into specific groups, classes, or categories. This process is paramount in drawing meaning from collections of ambiguous or unsorted data.

The Concept of Classification

Classification in machine learning is a supervised learning concept which basically categorises a set of data into classes. The most common classification problems are – speech recognition, handwriting recognition, biometric identification, document classification etc.

An interesting difference between classification and other strategies of data analysis is that the groups or classes are pre-determined. The data is analysed based on these established categories, making classification a form of supervised learning. Furthermore, this technique is frequently used in applications that require categorical rather than numerical predictions.

Types of Classification

There are several types of machine learning classification. These include binary classification, multiclass classification, multilabel classification and imbalanced classification.

  • Binary Classification: As the name suggests, this type of classification divides a dataset into two classes. It is the most straightforward type of classification, and familiar examples include spam email filtration and customer churn prediction.
  • Multiclass Classification: This is a classification method where more than two classes or categories are present. Examples include handwriting recognition and speech recognition, where the target variables are more than two.
  • Multilabel Classification: In a multilabel classification, an instance (or data point) can belong to multiple classes concurrently. The categories are not mutually exclusive, which means the presence of one class does not rule out the presence of another.
  • Imbalanced Classification: This type of classification refers to a classification problem where the class distribution of instances is uneven or imbalanced. For instance, fraud detection in banking and insurance, where the fraudulent transactions are far fewer than the legitimate ones.

Understanding Classification Algorithms

The process of classification involves a variety of algorithms. Different classification problems might require different techniques. Here is an overview of some common algorithms used:

  • Logistic Regression: Despite the name, logistic regression is a classification algorithm. It uses the logistic function to model the probabilities for binary classification problems.
  • Decision Trees: Decision Tree builds classification models in the form of a tree structure. It utilizes an if-then rule set which is mutually exclusive and exhaustive for classification. The rules are learned sequentially using the training data one at a time.
  • Random Forest: A Random Forest is a collection of decision trees that are all independently grown/ trained. Each tree votes for predicting the result and the category receiving the most votes is selected.
  • Support Vector Machines (SVM): This algorithm creates a boundary between clusters of data to categorise future points. It operates by maximizing the margin around the separating hyperplane.


Understanding the basics of classification in machine learning is essential to comprehend the overall discipline of machine learning. Classification provides a structure for data, converting it from a meaningless jumble into actionable insights. While there are various classification methods, each with its unique purpose, notable examples are binary classification, multiclass classification, and multilabel classification. These classification strategies are made possible by algorithms such as logistic regression, decision trees, random forests, and support vector machines. Thus, classification plays an indispensable role in machine learning and related fields and is a primary stepping stone towards comprehensive data analysis.


  1. What is classification in machine learning?
    Classification in machine learning is a supervised learning concept that basically categorises a set of data into classes.
  2. What are the types of classification?
    The types of classification include binary classification, multiclass classification, multilabel classification, and imbalanced classification.
  3. What is the difference between classification and regression in machine learning?
    Classification predicts the category to which a data point belongs, whereas regression predicts a numerical value based on previous observed data.
  4. What are the common algorithms used in classification?
    The common algorithms used in classification include Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines.
  5. What is multilabel classification?
    In a multilabel classification, a data point can belong to multiple classes concurrently, meaning the categories are not mutually exclusive.


Please enter your comment!
Please enter your name here

Related articles

"Exploring the Basics and Applications of Neural Networks"

In the realm of artificial intelligence and machine learning, neural networks are emerging as a popular choice due...

"Understanding the Basics of Decision Trees"

Decision Trees are a fundamental type of machine learning algorithm which is predominantly used...

"Understanding the Basics of Clustering Algorithms"

Understanding the Basics of Clustering Algorithms Clustering Algorithms are an essential machine learning methodology employed in many scientific fields...

"An In-Depth Understanding of Regression Analysis"

Regression analysis, a statistical model, enables researchers and analysts to comprehend and quantify the relationship between two or...