Classification in Machine Learning

What is Classification Techniques?

In the world of machine learning and statistics, classification is one type of supervised learning approach in which the computer program and algorithms learn from the data input given to it and then use this learning approach to classify new observations or datasets. This data set may simply be bi-class (like identifying whether the student will pass or fail, or that the mail is spam or non-spam) or it may be multi-class too.

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The objective of “Classification in Machine Learning “article

Let us look at some of the objectives covered under this section of classification in machine learning.

  1. What is Machine Learning Classification?
  2. Basic Terminology in Classification Algorithms
  3. Machine Learning Classification Example
  4. Applications of Classification Algorithms
  5. Types of Classification in Machine Learning

Basic Terminology in Classification Algorithms

Here is some terminology or keypoint you should learn for building a Classification Model.

  • Feature: A feature or input is an individual measurable property of a phenomenon being observed.
  • Classifier: An algorithm or model that maps the input data to a specific category.
  • Classification model: A classification model tries to draw some prediction from the input values given for training. It will predict the class for the new data.
  • Binary Classification: Classification task with two possible classes or outputs. E.g.: Email classification (Spam / Not Spam)
  • Multi-class classification: Classification with more than two classes or output. In multi-class classification, each sample data is assigned to one and only one target label. E.g.: An animal can be a cat or dog but not both at the same time.
  • Multi-label classification: Classification task where each data input is mapped to a set of target labels (more than one class). E.g.: A news article can be on a person, sports, and location at the same time.

Some examples of classification problems are:

classification in ml desireAI
Fig: Handwriting Recognition

Applications of Classification Algorithms

Some of the key areas where classification in Supervised Learning are being used:

  • To find whether an email received is spam or not spam
  • To identify customer segments
  • To find if a bank loan is granted
  • To identify if a kid will pass or fail in an examination
  • Bank customers loan pay willingness prediction.
  • Cancer tumor cells identification.
  • Sentiment analysis
  • Drugs classification
  • Facial keypoints detection
  • Pedestrians detection in an automotive car driving

Types of Classification Algorithms

Let’s go through with some types of Classification Algorithm in Machine Learning. Those model are given below.

1.Linear Models

  • Logistic Regression
  • Support Vector Machines

2.Nonlinear models

  • K-nearest Neighbors (KNN)
  • Kernel Support Vector Machines (SVM)
  • Naïve Bayes
  • Decision Tree Classification
  • Random Forest Classification


This completes the ‘Classification in Machine Learning’ tutorial. We have learned about different classification algorithms used in Machine Learning and application of Classification algorithms. In the upcoming articles, we will learn about different machine learning classification models or algorithms in details. Including their real-life use and how we implement those classification algorithms in a dataset.

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