Types of Classification Problems in Machine Learning

In Machine Learning, classification means predicting the class of data points. In simple words, when we are predicting the category of the target label using machine learning algorithms then this is known as classification. There are mainly 3 types of problems in classification. In this article, I will take you through the types of classification problems in machine learning.

Types of Classification Problems in Machine Learning

There are mainly 3 types of classification problems in machine learning:

  1. Binary Classification
  2. Multiclass Classification
  3. Multilabel classification

Let’s go through all these types of problems of classification to understand what they are and how they are different from each other.

Binary Classification:

Binary Classification is the most general type of classification problem. Here we have two classes in the target column and we have to predict the true class from both classes. In binary classification, we generally get the problem where one class is a positive class and one class is a negative class.

For example, the popular task of spam detection in the data science community is the task of binary classification. Some of the most popular algorithms that are most suitable in the problems of binary classification are:

  1. Logistic Regression
  2. K-Nearest Neighbour
  3. Naive Bayes
  4. Decision Tree

Multiclass Classification:

Multiclass classification is the problem of more than two classes. When the target column contains more than two classes and we have to predict the actual class from all the classes then it is the problem of multiclass classification.

For example, predicting the number 5 from the images of numbers between 1 and 10 is the problem of multiclass classification. Some of the best-suited algorithms for the problems of multiclass classification are:

  1. K-Nearest Neighbour
  2. Decision Tree
  3. Naive Bayes
  4. Random Forest

Multilabel Classification:

Both in binary and multiclass classification we have classes in one single target column. But in multilabel classification the scenario is different. When the target class labels are two or more than two then it is the problem of multilabel classification.

Some of the best algorithms for multilabel classification are:

  1. K-Nearest Neighbour
  2. Decision Tree
  3. SVM Classifier
  4. Random Forest

Also, Read – All Machine Learning Algorithms and Models Explained using Python.

Summary

When we classify between more than two classes, this is the problem of multiclass classification because classification between only 2 classes is a binary classification. If we assign a label to each class, then this is the problem of multilabel classification. I hope you liked this article on the types of problems in classification in machine learning. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
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