Articles → MACHINE LEARNING → Terminologies Used In Machine Learning

Terminologies Used In Machine Learning

This article describes the different terminologies used in Machine learning.

Mathematical Model

A mathematical model is a simplified representation of a real-world system using mathematical concepts, equations, and relationships. For instance, the Pythagorean theorem model calculates the length of a side in a right triangle.

Labelled Dataset

If the dataset contains both inputs and the correct outputs (labels), it is called a labelled dataset. Consider the following example: -

Picture showing sample data on which machine learning algorithm is applied

Each row is a patient record with input features (like age and blood sugar) and a label indicating whether the person has diabetes.

Supervised Learning

Supervised learning is a type of machine learning where the model is given input data and labels (output).

Unsupervised Learning

Unsupervised learning is a type of machine learning where the model is given input data without any labels (output). For example, you have the data on customers (age, spending habits, income), but no labels. You want to divide them into natural groups (e.g., budget shoppers vs premium buyers).

Independent And Dependent Variables

In machine learning, independent variables (features) are the inputs used to predict the dependent variable (target/output).

Problem Types

Classification Type

In classification-type problems, we categorize the target into fixed categories. For example, categorize the email as spam or not spam.

Regression Type

In regression-type problems, the target is continuous numerical values. It means that the output can take any value within a range (not limited to fixed categories or labels). There is no fixed or finite set of possible outputs.

If a regression problem involves predicting temperature and the expected values fall between 10°C and 20°C, then:

The range of possible outputs is 10 to 20.
Any value within this range — such as 10.5, 11.6, or 19.99 — is valid.
These values are continuous, meaning the model can predict any decimal or fractional number within that interval (not just whole numbers).

Posted By -	Karan Gupta

Posted On -	Friday, May 16, 2025

Query/Feedback

Your Email Id		**

Subject		*

Query/Feedback	Characters remaining 250	**